Adaptive predictor apparatus and methods

ABSTRACT

Apparatus and methods for training and operating of robotic devices. Robotic controller may comprise a predictor apparatus configured to generate motor control output. The predictor may be operable in accordance with a learning process based on a teaching signal comprising the control output. An adaptive controller block may provide control output that may be combined with the predicted control output. The predictor learning process may be configured to learn the combined control signal. Predictor training may comprise a plurality of trials. During initial trial, the control output may be capable of causing a robot to perform a task. During intermediate trials, individual contributions from the controller block and the predictor may be inadequate for the task. Upon learning, the control knowledge may be transferred to the predictor so as to enable task execution in absence of subsequent inputs from the controller. Control output and/or predictor output may comprise multi-channel signals.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/707,985, filed Sep. 18, 2017, which is a continuation of U.S. patentapplication Ser. No. 13/842,530, filed Mar. 15, 2013 each of theforegoing being incorporated herein by reference in its entirety.

COPYRIGHT

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever.

BACKGROUND Technological Field

The present disclosure relates to adaptive control and training ofrobotic devices.

Background

Robotic devices are used in a variety of applications, such asmanufacturing, medical, safety, military, exploration, and/or otherapplications. Some existing robotic devices (e.g., manufacturingassembly and/or packaging) may be programmed in order to perform desiredfunctionality. Some robotic devices (e.g., surgical robots) may beremotely controlled by humans, while some robots (e.g., iRobot Roomba®)may learn to operate via exploration.

Programming robots may be costly and remote control may require a humanoperator. Furthermore, changes in the robot model and/or environment mayrequire changes in the programming code. Remote control typically relieson user experience and/or agility that may be inadequate when dynamicsof the control system and/or environment (e.g., an unexpected obstacleappears in path of a remotely controlled vehicle) change rapidly.

SUMMARY

One aspect of the disclosure relates to a method of predicting a plantcontrol output by an adaptive computerized predictor apparatus. Themethod may comprise: configuring the predictor apparatus, using one ormore processors, to operate in accordance with a learning process basedon a teaching input; at a first time instance, based on a sensorycontext, causing the predictor apparatus to generate the plant controloutput; configuring the predictor apparatus, using one or moreprocessors, to provide the predicted plant control output as theteaching input into the learning process; and at a second time instancesubsequent to the first time instance, causing the predictor apparatusto generate the predicted plant control output based on the sensorycontext and the teaching input. The predicted plant control output maybe configured to cause the plant to perform an action consistent withthe sensory context.

In some implementations, the plant may comprise a robotic platform.Responsive to the sensory context comprising a representation of anobstacle, the action may comprise an avoidance maneuver executed by therobotic platform. Responsive to the sensory context comprising arepresentation of a target, the action may comprise an approach maneuverexecuted by the robotic platform.

In some implementations, the sensory context may be based on sensoryinput into the learning process. A portion of the sensory inputcomprising a video sensor data and another portion of the sensory inputmay comprise the predicted plant control output.

In some implementations, the learning process may be configured based ona network of computerized neurons configured to be adapted in accordancewith the sensory context and the teaching input.

In some implementations, multiple ones of the computerized neurons maybe interconnected by connections characterized by connection efficacy.The adaptation may comprise adapting the connection efficacy ofindividual connections based on the sensory context and the teachinginput.

In some implementations, the adaptation may be based on an error measurebetween the predicted plant control output and the teaching input.

In some implementations, individual ones of the computerized neurons maybe communicatively coupled to connections characterized by connectionefficacy. Individual ones of the computerized neurons may be configuredto be operable in accordance with a dynamic process characterized by anexcitability parameter. The sensory context may be based on input spikesdelivered to into the predictor apparatus via a portion of theconnections. Individual ones of the input spikes may be capable ofincreasing the excitability parameter associated with individual ones ofthe computerized neurons. The teaching input may comprise one or moreteaching spikes configured to adjust an efficacy of a portion of theconnections. The efficacy adjustment for a given connection may providea portion of the input spikes into a given computerized neuron beingconfigured based on one or more events occurring within a plasticitywindow. The one or more event may include one or more of: (i) a presenceof one or more input spikes on the given connection, (ii) an outputbeing generated by the given neuron, or (iii) an occurrence of at leastone of the one or more teaching spikes.

In some implementations, responsive to the sensory context being updatedat 40 ms intervals, the plasticity window duration may be selectedbetween 5 ms and 200 ms, inclusive.

In some implementations, a portion of the computerized neurons maycomprise spiking neurons. Individual ones of the spiking neurons may becharacterized by a neuron excitability parameter configured to determinean output spike generation by a corresponding spiking neuron. Multipleones of the spiking neurons may be interconnected by second connectionscharacterized by second connection efficacy. Individual ones of thesecond connections may be configured to communicate one or more spikesfrom a pre-synaptic spiking neuron to a post-synaptic spiking neuron. Aportion of the sensory context may be based on sensory input into thelearning process comprising one or more spikes.

In some implementations, the predicted plant control output may compriseone or more spikes generated based on spike outputs by individual onesof the spiking neurons.

In some implementations, the sensory input may comprise one or morespikes configured to be communicated by a portion of the connections.

In some implementations, the predicted plant control output may comprisea continuous signal configured based on one or more spike outputs byindividual ones of spiking neurons. The continuous signal may includeone or more of an analog signal, a polyadic signal with arity greaterthan one, an n-bit long discrete signal with n-bits greater than one, areal-valued signal, or a digital representation of a real-valued signal.

In some implementations, the sensory input may comprise a continuoussignal. The continuous signal may include one or more of an analogsignal, a polyadic signal with arity greater than 1, an n-bit longdiscrete signal with n-bits greater than 1, or a real-valued signal, ora digital representation of an analog signal.

In some implementations, the sensory input may comprise a binary signalcharacterized by a single bit.

In some implementations, the learning process may be configured to beupdated at regular time intervals. The adaptation may be based on anerror measure between (i) the predicted plant control output generatedat a given time instance and (ii) the teaching signal determined atanother given time instance prior to the given time instance. The giventime instance and the other time instance may be separated by a durationequal to one of the regular time intervals.

In some implementations, the plant may comprise at least one motorcomprising a motor interface. The predicted plant control output maycomprise one or more instructions to the motor interface configured toactuate the at least one motor.

In some implementations, the learning process may comprise supervisedlearning process.

In some implementations, the predicted plant control output may comprisea vector of outputs comprising two or more output components.

In some implementations, the learning process may be configured basedone or more a look up table, a hash-table, and a data base table. Thedata base table may be configured to store a relationship between agiven sensory context, a teaching input, associated with the givensensory context, and the predicted plant control output generated forthe given sensory context during learning.

Another aspect of the disclosure relates to a computerized predictorapparatus comprising a plurality of computer-readable instructionsconfigured to, when executed, generate a predicted control output by:configuring the predictor apparatus to operate in accordance with alearning process based on a teaching input; and based on a sensorycontext, causing the predictor apparatus to generate the predictedcontrol output. The teaching input may comprise the predicted controloutput.

Yet another aspect of the disclosure relates to a computerized roboticneuron network control apparatus. The apparatus may comprise one or moreprocessors configured to execute computer program modules. The computerprogram modules may comprise a first logic module and a second logicmodule. The first logic module may be configured to receive a sensoryinput signal and a teaching signal. The second logic module may beconfigured to generate a predicted output based on the sensory inputsignal and a teaching signal. The teaching signal may comprise anotherpredicted output generated prior to the predicted control output basedon the sensory input signal.

These and other objects, features, and characteristics of the presentinvention, as well as the methods of operation and functions of therelated elements of structure and the combination of parts and economiesof manufacture, will become more apparent upon consideration of thefollowing description and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of the limits of the invention. As usedin the specification and in the claims, the singular form of “a”, “an”,and “the” include plural referents unless the context clearly dictatesotherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram illustrating a robotic apparatus, accordingto one or more implementations.

FIG. 1B is a block diagram illustrating spiking neural network for usewith adaptive controller of FIG. 1A, in accordance with one or moreimplementations.

FIG. 2A is a block diagram illustrating an apparatus comprising anadaptable predictor block, according to one or more implementations.

FIG. 2B is a block diagram illustrating an apparatus comprising anadaptable predictor block operable in accordance with a teaching signal,according to one or more implementations.

FIG. 3A is a block diagram illustrating a multichannel adaptivepredictor apparatus, according to one or more implementations.

FIG. 3B is a block diagram illustrating an adaptive predictor apparatusconfigured to generate a multichannel output, according to one or moreimplementations.

FIG. 3C is a block diagram illustrating a multichannel adaptivepredictor configured to interface to a plurality of combiner apparatus,according to one or more implementations.

FIG. 3D is a block diagram illustrating a multiplexing adaptivepredictor configured to interface to a plurality of combiner apparatus,according to one or more implementations.

FIG. 4 is a block diagram illustrating structure of the adaptivepredictor apparatus of, for example, FIGS. 2A-3B, according to one ormore implementations.

FIG. 5 is a block diagram illustrating structure of artificial neuronnetwork useful for implementing adaptive predictor apparatus of arobotic device configured for obstacle avoidance training of, forexample, FIG. 7, according to one or more implementations.

FIG. 6 is a graphical illustration depicting obstacle avoidance trainingof a robotic device comprising an adaptive predictor apparatus of, forexample, FIG. 2B, according to one or more implementations.

FIG. 7 is a graphical illustration depicting obstacle approach/avoidancetraining of a robotic device comprising an adaptive predictor apparatusof, for example, FIG. 2B, according to one or more implementations.

FIG. 8A is a logical flow diagram illustrating a method of operating anadaptive predictor, in accordance with one or more implementations.

FIG. 8B is a logical flow diagram illustrating a method of training anadaptive predictor, in accordance with one or more implementations.

FIG. 8C is a logical flow diagram illustrating a method of operating anadaptive predictor based on input sensory context, in accordance withone or more implementations.

FIG. 9 is a logical flow diagram illustrating a method of operating anadaptive predictor comprising a training input, in accordance with oneor more implementations.

FIG. 10A is a block diagram illustrating a sensory processing apparatususeful with an adaptive controller of a robotic device of FIG. 10B, inaccordance with one or more implementations.

FIG. 10B is a graphical illustration depicting robotic apparatuscomprising an adaptive controller apparatus of the disclosure configuredfor obstacle avoidance, in accordance with one or more implementations.

FIG. 11A is a block diagram illustrating a computerized system usefulfor, inter alia, implementing adaptive predictor apparatus, inaccordance with one or more implementations.

FIG. 11B is a block diagram illustrating a neuromorphic computerizedsystem useful with, inter alia, an adaptive predictor methodology of thedisclosure, in accordance with one or more implementations.

FIG. 11C is a block diagram illustrating a hierarchical neuromorphiccomputerized system architecture useful with, inter alia, animplementing adaptive predictor apparatus, in accordance with one ormore implementations.

FIG. 11D is a block diagram illustrating cell-type neuromorphiccomputerized system architecture useful with, inter alia, implementingadaptive predictor apparatus, in accordance with one or moreimplementations.

FIG. 12 is a graphical illustration operation of a robotic controller,comprising an adaptive predictor of, e.g., FIGS. 3D-3E configured todevelop an association between control action and sensory context,according to one or more implementations.

FIG. 13A is a timing diagram illustrating operation of a combinerapparatus in an adaptive controller apparatus, in accordance with one ormore implementations.

FIG. 13B is a timing diagram illustrating operation of a combinerapparatus adapted to multiplex multichannel inputs, in accordance withone or more implementations.

FIG. 14 is a graphical illustration of learning a plurality of behaviorsover multiple trials behavior by an adaptive controller, e.g., of FIG.2A, in accordance with one or more implementations.

FIG. 15 is a plot illustrating performance of an adaptive predictorapparatus of, e.g., FIG. 3A, during training, in accordance with one ormore implementations.

FIG. 16 is a plot illustrating output of an adaptive predictorapparatus, e.g., of FIG. 3B, characterized by a cumulative performancefunction of an adaptive predictor apparatus of, e.g., FIG. 3A, duringtraining, in accordance with one or more implementations.

FIG. 17 is a logical flow diagram illustrating a method of developing ahierarchy of control tasks by a controller comprising an adaptivepredictor, in accordance with one or more implementations.

FIG. 18 is a logical flow diagram illustrating a method of operating asignal combiner for use with an adaptive predictor apparatus of FIG. 3A,in accordance with one or more implementations.

FIG. 19A is a logical flow diagram illustrating a method of developingan association between an action indication and sensory context for usein an adaptive predictor apparatus of, e.g., FIG. 20A, in accordancewith one or more implementations.

FIG. 19B is a logical flow diagram illustrating a method of operating anadaptive predictor apparatus of, e.g., FIG. 20B using associationsbetween an action indication and sensory context for, in accordance withone or more implementations.

FIG. 20A is a block diagram illustrating an adaptive predictorconfigured to develop an association between control action and sensorycontext, according to one or more implementations.

FIG. 20B is a block diagram illustrating a control system comprising anadaptive predictor configured to generate control signal based on anassociation between control action and sensory context, according to oneor more implementations.

FIG. 21 illustrates an exemplary embodiment of a hierarchy of actionsfor use with a controller illustrated in FIG. 20B.

All Figures disclosed herein are © Copyright 2013 Brain Corporation. Allrights reserved.

DETAILED DESCRIPTION

Implementations of the present technology will now be described indetail with reference to the drawings, which are provided asillustrative examples so as to enable those skilled in the art topractice the technology. Notably, the figures and examples below are notmeant to limit the scope of the present disclosure to a singleimplementation, but other implementations are possible by way ofinterchange of or combination with some or all of the described orillustrated elements. Wherever convenient, the same reference numberswill be used throughout the drawings to refer to same or like parts.

Where certain elements of these implementations can be partially orfully implemented using known components, only those portions of suchknown components that are necessary for an understanding of the presentinvention will be described, and detailed descriptions of other portionsof such known components will be omitted so as not to obscure thedisclosure.

In the present specification, an implementation showing a singularcomponent should not be considered limiting; rather, the invention isintended to encompass other implementations including a plurality of thesame component, and vice-versa, unless explicitly stated otherwiseherein.

Further, the present disclosure encompasses present and future knownequivalents to the components referred to herein by way of illustration.

As used herein, the term “bus” is meant generally to denote all types ofinterconnection or communication architecture that is used to access thesynaptic and neuron memory. The “bus” may be optical, wireless,infrared, and/or another type of communication medium. The exacttopology of the bus could be for example standard “bus”, hierarchicalbus, network-on-chip, address-event-representation (AER) connection,and/or other type of communication topology used for accessing, e.g.,different memories in pulse-based system.

As used herein, the terms “computer”, “computing device”, and“computerized device” may include one or more of personal computers(PCs) and/or minicomputers (e.g., desktop, laptop, and/or other PCs),mainframe computers, workstations, servers, personal digital assistants(PDAs), handheld computers, embedded computers, programmable logicdevices, personal communicators, tablet computers, portable navigationaids, J2ME equipped devices, cellular telephones, smart phones, personalintegrated communication and/or entertainment devices, and/or any otherdevice capable of executing a set of instructions and processing anincoming data signal.

As used herein, the term “computer program” or “software” may includeany sequence of human and/or machine cognizable steps which perform afunction. Such program may be rendered in a programming language and/orenvironment including one or more of C/C++, C #, Fortran, COBOL,MATLAB™, PASCAL, Python, assembly language, markup languages (e.g.,HTML, SGML, XML, VoXML), object-oriented environments (e.g., CommonObject Request Broker Architecture (CORBA)), Java™ (e.g., J2ME, JavaBeans), Binary Runtime Environment (e.g., BREW), and/or otherprogramming languages and/or environments.

As used herein, the terms “connection”, “link”, “transmission channel”,“delay line”, “wireless” may include a causal link between any two ormore entities (whether physical or logical/virtual), which may enableinformation exchange between the entities.

As used herein, the term “memory” may include an integrated circuitand/or other storage device adapted for storing digital data. By way ofnon-limiting example, memory may include one or more of ROM, PROM,EEPROM, DRAM, Mobile DRAM, SDRAM, DDR/2 SDRAM, EDO/FPMS, RLDRAM, SRAM,“flash” memory (e.g., NAND/NOR), memristor memory, PSRAM, and/or othertypes of memory.

As used herein, the terms “integrated circuit”, “chip”, and “IC” aremeant to refer to an electronic circuit manufactured by the patterneddiffusion of trace elements into the surface of a thin substrate ofsemiconductor material. By way of non-limiting example, integratedcircuits may include field programmable gate arrays (e.g., FPGAs), aprogrammable logic device (PLD), reconfigurable computer fabrics (RCFs),application-specific integrated circuits (ASICs), and/or other types ofintegrated circuits.

As used herein, the terms “microprocessor” and “digital processor” aremeant generally to include digital processing devices. By way ofnon-limiting example, digital processing devices may include one or moreof digital signal processors (DSPs), reduced instruction set computers(RISC), general-purpose (CISC) processors, microprocessors, gate arrays(e.g., field programmable gate arrays (FPGAs)), PLDs, reconfigurablecomputer fabrics (RCFs), array processors, secure microprocessors,application-specific integrated circuits (ASICs), and/or other digitalprocessing devices. Such digital processors may be contained on a singleunitary IC die, or distributed across multiple components.

As used herein, the term “network interface” refers to any signal, data,and/or software interface with a component, network, and/or process. Byway of non-limiting example, a network interface may include one or moreof FireWire (e.g., FW400, FW800, etc.), USB (e.g., USB2), Ethernet(e.g., 10/100, 10/100/1000 (Gigabit Ethernet), 10-Gig-E, etc.), MoCA,Coaxsys (e.g., TVnet™), radio frequency tuner (e.g., in-band or OOB,cable modem, etc.), Wi-Fi (802.11), WiMAX (802.16), PAN (e.g., 802.15),cellular (e.g., 3G, LTE/LTE-A/TD-LTE, GSM, etc.), IrDA families, and/orother network interfaces.

As used herein, the terms “node”, “neuron”, and “neuronal node” aremeant to refer, without limitation, to a network unit (e.g., a spikingneuron and a set of synapses configured to provide input signals to theneuron) having parameters that are subject to adaptation in accordancewith a model.

As used herein, the terms “state” and “node state” is meant generally todenote a full (or partial) set of dynamic variables (e.g., a membranepotential, firing threshold and/or other) used to describe state of anetwork node.

As used herein, the term “synaptic channel”, “connection”, “link”,“transmission channel”, “delay line”, and “communications channel”include a link between any two or more entities (whether physical (wiredor wireless), or logical/virtual) which enables information exchangebetween the entities, and may be characterized by a one or morevariables affecting the information exchange.

As used herein, the term “Wi-Fi” includes one or more of IEEE-Std.802.11, variants of IEEE-Std. 802.11, standards related to IEEE-Std.802.11 (e.g., 802.11 a/b/g/n/s/v), and/or other wireless standards.

As used herein, the term “wireless” means any wireless signal, data,communication, and/or other wireless interface. By way of non-limitingexample, a wireless interface may include one or more of Wi-Fi,Bluetooth, 3G (3GPP/3GPP2), HSDPA/HSUPA, TDMA, CDMA (e.g., IS-95A,WCDMA, etc.), FHSS, DSSS, GSM, PAN/802.15, WiMAX (802.16), 802.20,narrowband/FDMA, OFDM, PCS/DCS, LTE/LTE-A/TD-LTE, analog cellular, CDPD,satellite systems, millimeter wave or microwave systems, acoustic,infrared (i.e., IrDA), and/or other wireless interfaces.

FIG. 1A illustrates one implementation of an adaptive robotic apparatusfor use with the adaptive predictor methodology described hereinafter.The apparatus 100 of FIG. 1A may comprise an adaptive controller 102 anda plant (e.g., robotic platform) 110. The controller 102 may beconfigured to generate control output 108 for the plant 110. The output108 may comprise one or more motor commands (e.g., pan camera to theright), sensor acquisition parameters (e.g., use high resolution cameramode), commands to the wheels, arms, and/or other actuators on therobot, and/or other parameters. The output 108 may be configured by thecontroller 102 based on one or more sensory inputs 106. The input 106may comprise data used for solving a particular control task. In one ormore implementations, such as those involving a robotic arm orautonomous robot, the signal 106 may comprise a stream of raw sensordata and/or preprocessed data. Raw sensor data may include dataconveying information associated with one or more of proximity,inertial, terrain imaging, and/or other information. Preprocessed datamay include data conveying information associated with one or more ofvelocity, information extracted from accelerometers, distance toobstacle, positions, and/or other information. In some implementations,such as those involving object recognition, the signal 106 may comprisean array of pixel values in the input image, or preprocessed data. Pixeldata may include data conveying information associated with one or moreof RGB, CMYK, HSV, HSL, grayscale, and/or other information.Preprocessed data may include data conveying information associated withone or more of levels of activations of Gabor filters for facerecognition, contours, and/or other information. In one or moreimplementations, the input signal 106 may comprise a target motiontrajectory. The motion trajectory may be used to predict a future stateof the robot on the basis of a current state and the target state. Inone or more implementations, the signals in FIG. 1A may be encoded asspikes.

The controller 102 may be operable in accordance with a learning process(e.g., reinforcement learning and/or supervised learning). In one ormore implementations, the controller 102 may optimize performance (e.g.,performance of the system 100 of FIG. 1A) by minimizing average value ofa performance function as described in detail in co-owned U.S. patentapplication Ser. No. 13/487,533, entitled “STOCHASTIC SPIKING NETWORKLEARNING APPARATUS AND METHODS”, incorporated herein by reference in itsentirety.

FIG. 1B illustrates an artificial neuron configured to implement thelearning process of adaptive controller (e.g., 102 of FIG. 1A). Thenetwork 120 may comprise at least one spiking neuron 140, operableaccording to, for example, a Spike Response Process (SRP) denoted byblock 130 in FIG. 1B. The neuron 140 may receive M-dimensional inputstream X(t) 122 via connections 124. In some implementations, theM-dimensional stream may correspond to M-input synaptic connections 124into the neuron 140. As shown in FIG. 1B, individual input connections124 may be characterized by a connection parameter 126 eij. Theparameter hat 6 may be referred to as the learning parameter andconfigured to be adjusted during learning. In one or moreimplementation, the learning parameter may comprise connection efficacy(e.g., weight). In some implementations, the learning parameter maycomprise transmission (e.g., synaptic) delay. In some implementations,the parameter 126 may comprise probability of spike transmission via therespective connection.

In some implementations, the neuron 140 may be configured to receiveexternal input via the connection 134. In one or more implementations,the input 134 may comprise training input. In some implementations ofsupervised learning, the training input 134 may comprise a supervisoryspike that may be used to trigger neuron post-synaptic response.

The neuron 140 may be configured to generate output y(t) (e.g., apost-synaptic spike) that may be delivered to the desired targets (e.g.,other neurons of the network, not shown) via one or more outputconnections (e.g., 144 in FIG. 1B). As shown in FIG. 1B, individualoutput connections 144 may be characterized by a connection parameter146 that may be adjusted during learning. In one or more implementation,the connection parameter 146 may comprise connection efficacy (e.g.,weight). In some implementations, the parameter 146 may comprisesynaptic delay. In some implementations, the parameter 146 may comprisespike transmission probability.

The neuron 140 may be configured to implement controller functionality,such as described for example in U.S. patent application Ser. No.13/487,533, entitled “STOCHASTIC SPIKING NETWORK LEARNING APPARATUS ANDMETHODS”, filed Jun. 4, 2012, incorporated supra, in order to control,for example, a robotic arm. The output signal y(t) may include motorcontrol commands configured to move a robotic arm along a targettrajectory. The process 130 may be characterized by internal state q.The internal state q may, for example, comprise a membrane voltage ofthe neuron, conductance of the membrane, and/or other parameters. Theprocess 130 may be characterized by one or more learning parameter whichmay comprise input connection efficacy, 126, output connection efficacy146, training input connection efficacy 136, response generating(firing) threshold, resting potential of the neuron, and/or otherparameters. In one or more implementations, some learning parameters maycomprise probabilities of signal transmission between the units (e.g.,neurons) of the network.

In some implementations, the training input (e.g., 134 in FIG. 1B) maybe differentiated from sensory inputs (e.g., provided via theconnections 124) to the neuron 140 as follows. During learning: data(e.g., spike events) arriving to the neuron 140 via the connections 124may cause changes in the neuron state (e.g., increase neuron membranepotential and/or other parameters). Changes in the neuron state maycause the neuron to generate a response (e.g., output a spike). Teachingdata arriving to the neuron 140 via the connection 134 may cause (i)changes in the neuron dynamic model (e.g., modify parameters a,b,c,d ofIzhikevich neuron model, described for example in co-owned U.S. patentapplication Ser. No. 13/623,842, entitled “SPIKING NEURON NETWORKADAPTIVE CONTROL APPARATUS AND METHODS”, filed Sep. 20, 2012,incorporated herein by reference in its entirety); and/or (ii)modification of connection efficacy, based, for example, on timing ofinput spikes, teacher spikes, and/or output spikes. In someimplementations, teaching data may trigger neuron output in order tofacilitate learning. In some implementations, teaching signal may becommunicated to other components of the control system.

During operation (e.g., subsequent to learning): data (e.g., spikeevents) arriving to the neuron 140 via the connections 124 may causechanges in the neuron state (e.g., increase neuron membrane potentialand/or other parameters). Changes in the neuron state may cause theneuron to generate a response (e.g., output a spike). Teaching data maybe absent during operation, while input data are required for the neuronto generate output.

Connections 124 in FIG. 1B may communicate one or more spiking and/oranalog inputs. As used herein the term ‘spiking’ signal may be used todescribe signals comprising one or more discrete events. In someimplementations, a spiking signal may comprise a stream of bits wherevalue of ‘1’ may be used to indicate individual events. In someimplementations, spiking signal may comprise one or more messages(having for example a time stamp associated therewith) corresponding toindividual events.

As used herein the term ‘non-spiking’ and/or ‘analog’ signal may be usedto describe real world continuous signals. In some implementations, thenon-spiking signal may comprise an analog signal (e.g., a voltage and/ora current produced by a source). In one or more implementations, thenon-spiking signal may comprise a digitized signal (e.g., sampled atregular intervals (sampling rate) with a given resolution). In someimplementations, the continuous signal may include one or more of ananalog signal, a polyadic signal with arity greater than 2, an n-bitlong discrete signal with n-bits greater than 2, a real-valued signal,and/or other continuous signal.

In one or more implementations, such as object recognition, and/orobstacle avoidance, the input 122 may comprise a stream of pixel valuesassociated with one or more digital images (e.g., video, radar,sonography, x-ray, magnetic resonance imaging, and/or other types).Pixel data may include data conveying information associated with one ormore of RGB, CMYK, HSV, HSL, grayscale, and/or other information. Pixelsand/or groups of pixels associated with objects and/or features in theinput frames may be encoded using, for example, latency encodingdescribed in U.S. patent application Ser. No. 12/869,583, filed Aug. 26,2010 and entitled “INVARIANT PULSE LATENCY CODING SYSTEMS AND METHODS”;U.S. Pat. No. 8,315,305, issued Nov. 20, 2012, entitled “SYSTEMS ANDMETHODS FOR INVARIANT PULSE LATENCY CODING”; U.S. patent applicationSer. No. 13/152,084, filed Jun. 2, 2011, entitled “APPARATUS AND METHODSFOR PULSE-CODE INVARIANT OBJECT RECOGNITION”; and/or latency encodingcomprising a temporal winner take all mechanism described U.S. patentapplication Ser. No. 13/757,607, filed Feb. 1, 2013 and entitled“TEMPORAL WINNER TAKES ALL SPIKING NEURON NETWORK SENSORY PROCESSINGAPPARATUS AND METHODS”, each of the foregoing being incorporated hereinby reference in its entirety.

In one or more implementations, object recognition and/or classificationmay be implemented using spiking neuron classifier comprisingconditionally independent subsets as described in co-owned U.S. patentapplication Ser. No. 13/756,372 filed Jan. 31, 2013, and entitled“SPIKING NEURON CLASSIFIER APPARATUS AND METHODS” and/or co-owned U.S.patent application Ser. No. 13/756,382 filed Jan. 31, 2013, and entitled“REDUCED LATENCY SPIKING NEURON CLASSIFIER APPARATUS AND METHODS”, eachof the foregoing being incorporated herein by reference in its entirety.

In one or more implementations, encoding may comprise adaptiveadjustment of neuron parameters, such neuron excitability described inU.S. patent application Ser. No. 13/623,820 entitled “APPARATUS ANDMETHODS FOR ENCODING OF SENSORY DATA USING ARTIFICIAL SPIKING NEURONS”,filed Sep. 20, 2012, the foregoing being incorporated herein byreference in its entirety.

In some implementations, analog inputs may be converted into spikesusing, for example, kernel expansion techniques described in co pendingU.S. patent application Ser. No. 13/623,842 filed Sep. 20, 2012, andentitled “SPIKING NEURON NETWORK ADAPTIVE CONTROL APPARATUS ANDMETHODS”, the foregoing being incorporated herein by reference in itsentirety. In one or more implementations, analog and/or spiking inputsmay be processed by mixed signal spiking neurons, such as U.S. patentapplication Ser. No. 13/313,826 entitled “APPARATUS AND METHODS FORIMPLEMENTING LEARNING FOR ANALOG AND SPIKING SIGNALS IN ARTIFICIALNEURAL NETWORKS”, filed Dec. 7, 2011, and/or co-pending U.S. patentapplication Ser. No. 13/761,090 entitled “APPARATUS AND METHODS FORIMPLEMENTING LEARNING FOR ANALOG AND SPIKING SIGNALS IN ARTIFICIALNEURAL NETWORKS”, filed Feb. 6, 2013, each of the foregoing beingincorporated herein by reference in its entirety.

The learning parameters associated with the input/output connections(e.g., the parameters 126, 136, 146) may be adjusted in accordance withone or more rules, denoted in FIG. 1B by broken arrows 128, 138, 148,respectively.

The rules may be configured to implement synaptic plasticity in thenetwork. In some implementations, the plastic rules may comprise one ormore spike-timing dependent plasticity, such as rule comprising feedbackdescribed in co-owned and co-pending U.S. patent application Ser. No.13/465,903 entitled “SENSORY INPUT PROCESSING APPARATUS IN A SPIKINGNEURAL NETWORK”, filed May 7, 2012; rules configured to modify of feedforward plasticity due to activity of neighboring neurons, described inco-owned U.S. patent application Ser. No. 13/488,106, entitled “SPIKINGNEURON NETWORK APPARATUS AND METHODS”, filed Jun. 4, 2012; conditionalplasticity rules described in U.S. patent application Ser. No.13/541,531, entitled “CONDITIONAL PLASTICITY SPIKING NEURON NETWORKAPPARATUS AND METHODS”, filed Jul. 3, 2012; plasticity configured tostabilize neuron response rate as described in U.S. patent applicationSer. No. 13/691,554, entitled “RATE STABILIZATION THROUGH PLASTICITY INSPIKING NEURON NETWORK”, filed Nov. 30, 2012; activity-based plasticityrules described in co-owned U.S. patent application Ser. No. 13/660,967,entitled “APPARATUS AND METHODS FOR ACTIVITY-BASED PLASTICITY IN ASPIKING NEURON NETWORK”, filed Oct. 25, 2012, U.S. patent applicationSer. No. 13/660,945, entitled “MODULATED PLASTICITY APPARATUS ANDMETHODS FOR SPIKING NEURON NETWORKS”, filed Oct. 25, 2012; multi-modalrules described in U.S. patent application Ser. No. 13/763,005, entitled“SPIKING NETWORK APPARATUS AND METHOD WITH BIMODAL SPIKE-TIMINGDEPENDENT PLASTICITY”, filed Feb. 8, 2013, each of the foregoing beingincorporated herein by reference in its entirety.

In one or more implementations, neuron operation may be configured basedon one or more inhibitory connections providing input configured todelay and/or depress response generation by the neuron, as described inU.S. patent application Ser. No. 13/660,923, entitled “ADAPTIVEPLASTICITY APPARATUS AND METHODS FOR SPIKING NEURON NETWORK”, filed Oct.25, 2012, the foregoing being incorporated herein by reference in itsentirety

Connection efficacy updated may be effectuated using a variety ofapplicable methodologies such as, for example, event based updatesdescribed in detail in co-owned U.S. patent application Ser. No. 13/239,filed Sep. 21, 2011, entitled “APPARATUS AND METHODS FOR SYNAPTIC UPDATEIN A PULSE-CODED NETWORK”; 201220, U.S. patent application Ser. No.13/588,774, entitled “APPARATUS AND METHODS FOR IMPLEMENTING EVENT-BASEDUPDATES IN SPIKING NEURON NETWORK”, filed Aug. 17, 2012; and U.S. patentapplication Ser. No. 13/560,891 entitled “APPARATUS AND METHODS FOREFFICIENT UPDATES IN SPIKING NEURON NETWORKS”, each of the foregoingbeing incorporated herein by reference in its entirety.

Neuron process 130 may comprise one or more learning rules configured toadjust neuron state and/or generate neuron output in accordance withneuron inputs (e.g., 122, 124 in FIG. 1B).

In some implementations, the one or more leaning rules may comprisestate dependent learning rules described, for example, in U.S. patentapplication Ser. No. 13/560,902, entitled “APPARATUS AND METHODS FORSTATE-DEPENDENT LEARNING IN SPIKING NEURON NETWORKS”, filed Jul. 27,2012 and/or pending U.S. patent application Ser. No. 13/722,769 filedDec. 20, 2012, and entitled “APPARATUS AND METHODS FOR STATE-DEPENDENTLEARNING IN SPIKING NEURON NETWORKS”, each of the foregoing beingincorporated herein by reference in its entirety.

In one or more implementations, the one or more leaning rules may beconfigured to comprise one or more reinforcement learning, unsupervisedlearning, and/or supervised learning as described in co-owned andco-pending U.S. patent application Ser. No. 13/487,499 entitled“STOCHASTIC APPARATUS AND METHODS FOR IMPLEMENTING GENERALIZED LEARNINGRULES, incorporated supra, in accordance with focused exploration rulessuch as described, for example, in U.S. patent application Ser. No.13/489,280 entitled “APPARATUS AND METHODS FOR REINFORCEMENT LEARNING INARTIFICIAL NEURAL NETWORKS”, filed Jun. 5, 2012, the foregoing beingincorporated herein by reference in its entirety.

Adaptive controller (e.g., the controller apparatus 102 of FIG. 1A) maycomprise an adaptable predictor block configured to, inter alia, predictcontroller output (e.g., 108) based on the sensory input (e.g., 106 inFIG. 1A) and teaching input (e.g., 104 in FIG. 1A). FIGS. 2-3 illustrateexemplary adaptive predictor configurations in accordance with one ormore implementations.

FIGS. 2A-2B illustrate exemplary adaptive controllers comprising alearning predictor in accordance with one or more implementations. Theadaptive apparatus 200 of FIG. 2A may comprise a control block 212, anadaptive predictor 202, and a combiner 214. The control block 212, thepredictor 202 and the combiner 214 may cooperate to produce a controlsignal 224 for the plant 210. In one or more implementations, thecontrol signal 224 may comprise one or more motor commands (e.g., pancamera to the right, turn wheel to the left), sensor acquisitionparameters (e.g., use high resolution camera mode), and/or otherparameters.

The control block 212 may be configured to generate controller output u208 based on one or more of (i) sensory input (denoted 206 in FIG. 2A)and plant feedback 2162. In some implementations, plant feedback maycomprise proprioceptive signals, such as the readings from servo motors,joint position, and/or torque. In some implementations, the sensoryinput 206 may correspond to the controller sensory input 106, describedwith respect to FIG. 1A, supra.

The adaptive predictor 202 may be configured to generate predictedcontroller output uP 218 based on one or more of (i) the sensory input206 and the plant feedback 216_1. The predictor 202 may be configured toadapt its internal parameters, e.g., according to a supervised learningrule, and/or other machine learning rules.

The sensory input and/or the plant feedback may collectively be referredto as sensory context. The context may be utilized by the predictor 202in order to produce the predicted output 218. By way of a non-limitingillustration of obstacle avoidance by an autonomous rover, an image ofan obstacle (e.g., wall representation in the sensory input 206) may becombined with rover motion (e.g., speed and/or direction) to generateContext A. When the Context A is encountered, the control output 224 maycomprise one or more commands configured to avoid a collision betweenthe rover and the obstacle. Based on one or more prior encounters of theContext A—avoidance control output, the predictor may build anassociation between these events as described below.

The combiner 214 may implement a transfer function hO configured tocombine the raw controller output 208 and the predicted controlleroutput 218. In some implementations, the combiner 214 operation may beexpressed as follows:{circumflex over (u)}=h(u,u ^(P)).  (Eqn. 1)

Various realization of the transfer function of Eqn. 1 may be utilized.In some implementations, the transfer function may comprise additionoperation, union, a logical ‘AND’ operation, and/or other operations.

In one or more implementations, the transfer function may comprise aconvolution operation. In spiking network realizations of the combinerfunction, the convolution operation may be supplemented by use of afinite support kernel such as Gaussian, rectangular, exponential, and/orother finite support kernel. Such a kernel may implement a low passfiltering operation of input spike train(s). In some implementations,the transfer function may be characterized by a commutative propertyconfigured such that:û=h(u,u ^(P))=h(u ^(P) ,u).  (Eqn. 2)

In one or more implementations, the transfer function of the combiner214 may be configured as follows:h(0,u ^(P))=u ^(P).  (Eqn. 3)

In one or more implementations, the transfer function h may beconfigured as:h(u,0)=u.  (Eqn. 4)

In some implementations, the transfer function h may be configured as acombination of realizations of Eqn. 3-Eqn. 4 as:h(0,u ^(P))=u ^(P), and h(u,0)=u,  (Eqn. 5)

In one exemplary implementation, the transfer function satisfying Eqn. 5may be expressed as:h(u,u ^(P))=(1−u)×(1−u ^(P))−1.  (Eqn. 6)

In some implementations, the combiner transfer function may becharacterized by a delay expressed as:û(t _(i+1))=h(u(t _(i)),u ^(P)(t _(i))).  (Eqn. 7)

In Eqn. 7, _(û(ti+1)) denotes combined output (e.g., 220 in FIG. 2A) attime t+4t. As used herein, symbol t_(N) may be used to refer to a timeinstance associated with individual controller update events (e.g., asexpressed by Eqn. 7), for example t_(i) denoting time of the firstcontrol output, e.g., a simulation time step and/or a sensory inputframe step. In some implementations of training autonomous roboticdevices (e.g., rovers, bi-pedaling robots, wheeled vehicles, aerialdrones, robotic limbs, and/or other robotic devices), the updateperiodicity At may be configured to be between 1 ms and 1000 ms. It willbe appreciated by those skilled in the arts that various otherrealizations of the transfer function of the combiner 214 (e.g.,comprising a Heaviside step function, a sigmoidal function, such as thehyperbolic tangent, Gauss error function, or logistic function, and/or astochastic operation) may be applicable.

FIG. 2B illustrates a control apparatus 220 comprising an adaptablepredictor block 222 operable in accordance with a learning process thatis based on a teaching signal, according to one or more implementations.

The learning process of the adaptive predictor 222 may comprisesupervised learning process, reinforcement learning process, and/or acombination thereof. The learning process of the predictor 222 may beconfigured to generate predictor output 238. The control block 212, thepredictor 222, and the combiner 234 may cooperate to produce a controlsignal 240 for the plant 210. In one or more implementations, thecontrol signal 240 may comprise one or more motor commands (e.g., pancamera to the right, turn wheel to the left), sensor acquisitionparameters (e.g., use high resolution camera mode), and/or otherparameters.

The adaptive predictor 222 may be configured to generate predictedcontroller output u^(P) 238 based on one or more of (i) the sensoryinput 206 and the plant feedback 236. Predictor realizations, comprisingplant feedback (e.g., 216_1, 236 in FIGS. 2A-2B, respectively), may beemployed in applications such as, for example, wherein (i) the controlaction may comprise a sequence of purposefully timed commands (e.g.,associated with approaching a stationary target (e.g., a cup) by arobotic manipulator arm); and (ii) the plant may be characterized by aplant state time parameter (e.g., arm inertia, and/or motor responsetime) that may be greater than the rate of action updates. Parameters ofa subsequent command within the sequence may depend on the plant state(e.g., the exact location and/or position of the arm joints) that maybecome available to the predictor via the plant feedback.

Operation of the predictor 222 learning process may be aided by ateaching signal 204. As shown in FIG. 2B, the teaching signal 204 maycomprise the output 240 of the combiner:u ^(d) =ü.  (Eqn. 8)

In some implementations wherein the combiner transfer function may becharacterized by a delay ti (e.g., Eqn. 7), the teaching signal at timet_(i) may be configured based on values of u, u^(P) at a prior timet,__(i), for example as:u ^(d)(t _(i))=h(u(t _(i−1)),u ^(P)(t _(i−1))).  (Eqn. 9)

The training signal u^(d) at time t, may be utilized by the predictor inorder to determine the predicted output u^(P) at a subsequent time ti+i,corresponding to the context (e.g., the sensory input x) at time t:u ^(P)(t _(i+1))=F[x _(i) ,W(u ^(d)(t _(i)))].  (Eqn. 10)In Eqn. 10, the function W may refer to a learning process implementedby the predictor.

In one or more implementations wherein the predictor may comprise aspiking neuron network (e.g., the network 120 of FIG. 1B) operable inaccordance with a learning process, the training signal u^(d)=S^(d) maybe used to adjust one or more operational parameters 0 of the learningprocess, as described for example in co-owned U.S. patent applicationSer. No. 13/761,090, entitled “APPARATUS AND METHODS FOR IMPLEMENTINGLEARNING FOR ANALOG AND SPIKING SIGNALS IN ARTIFICIAL NEURAL NETWORKS”,filed Feb. 6, 2013, incorporated supra:

$\begin{matrix}{{\frac{d\;{\theta_{jk}(t)}}{dt} = {{\eta(t)}\left( {{\overset{\_}{S_{j}^{d}}(t)} - {{\overset{\_}{S}}_{j}(t)}} \right){\overset{\_}{S_{k}}(t)}}},} & \left( {{Eqn}.\mspace{14mu} 11} \right)\end{matrix}$where:63k(t) is the efficacy of the synaptic connection from the pre-synapticneuron i to neuron j; /(t) is the learning rate;S_(j)d(t) is low-pass filtered version of the target spike train forneuron j, with a filter time constant i^(d);(t) is the low-pass filtered version of the output spike train fromneuron j, with a filter time constant T; andS(t) is the low-pass filtered version of the i-th input spike train toneuron j, with a filter time constant

In some implementations (including the implementation of Eqn. 11), thelow-pass filtered version of the spike train may be expressed as:S _(k)(t)=∫₀ ^(∞) a _(k)(s)S _(k)(t−s)ds,  (Eqn. 12)with a(s) being a smoothing kernel. In one or more variants, thesmoothing kernel may comprise an exponential, Gaussian, and/or anotherfunction of time, configured using one or more parameters. Further, theparameters may comprise a filter time constant T. An example of anexponential smoothing kernel is:a _(k)(s)=exp(−s/τ),  (Eqn. 13)where z is the kernel time constant.

In one or more implementations, the learning rate_(i) of Eqn. 11 may beconfigured to vary with time, as described in detail in co-pending U.S.patent application Ser. No. 13/722,769 filed Dec. 20, 2012, and entitled“APPARATUS AND METHODS FOR STATE-DEPENDENT LEARNING IN SPIKING NEURONNETWORKS”, the foregoing being incorporated herein in its entirety.

Returning now to FIG. 2B, the combiner 234 may implement a transferfunction hO configured to combine the raw controller output 208 and thepredicted controller output 238. In various implementations, operationof the combiner 234 may be configured in accordance with Eqn. 1-Eqn. 7,described above and/or other relations. In one such realization, thecombiner 234 transfer function may be configured according to Eqn. 14,thereby implementing an additive feedback. In other words, output of thepredictor (e.g., 238) may be additively combined with the output of thecontroller (208) and the combined signal 240 may be used as the teachinginput (204) for the predictor. In some implementations, e.g., signal 366shown in FIGS. 3C-3D, the combined signal 240 may be utilized as aninput (context) signal into the predictor.

In one or more implementations, such as illustrated in FIGS. 2A-2B, thesensory input 206, the controller output 208, the predicted output 218,238, the combined output 224, 240 and/or plant feedback 216, 236 maycomprise spiking signal, analog signal, and/or a combination thereof.Analog to spiking and/or spiking to analog signal conversion may beeffectuated using, mixed signal spiking neuron networks, such as, forexample, described in U.S. patent application Ser. No. 13/313,826entitled “APPARATUS AND METHODS FOR IMPLEMENTING LEARNING FOR ANALOG ANDSPIKING SIGNALS IN ARTIFICIAL NEURAL NETWORKS”, filed Dec. 7, 2011,and/or co-pending U.S. patent application Ser. No. 13/761,090 entitled“APPARATUS AND METHODS FOR IMPLEMENTING LEARNING FOR ANALOG AND SPIKINGSIGNALS IN ARTIFICIAL NEURAL NETWORKS”, filed Feb. 6, 2013, incorporatedsupra.

Exemplary operation of the adaptive control system (e.g., 200, 220 ofFIGS. 2A-2B) is not described in detail. The predictor and/or thecontroller of the adaptive system 200, 220 may be operated in accordancewith an update process configured to be effectuated continuously and/orat discrete time intervals At, described above with respect to Eqn. 7.

The control output (e.g., 224 in FIG. 2A) may be provided at a ratebetween 1 Hz and 1000 Hz. A time scales T_(ptit) describing dynamics ofthe respective plant (e.g., response time of a rover and/or an aerialdrone platform, also referred to as the behavioral time scale) may varywith the plant type and comprise scales on the order of a second (e.g.,between 0.1 s to 2 s).

The transfer function of the combiner of the exemplary implementation ofthe adaptive system 200, 220, described below, may be configured asfollows:û=h(u,u ^(P))=u+u ^(P).  (Eqn. 14)

Training of the adaptive predictor (e.g., 202 of the control system 200of FIG. 2A) may be effectuated via a plurality of trials. In someimplementations, training of a mechanized robot and/or an autonomousrover may comprise between 5 and 50 trials. Individual trials may beconfigured with duration that may be sufficient to observe behavior ofthe plant (e.g., execute a turn and/or another maneuver), e.g., between1 and 10 s.

In some implementations the trial duration may last longer (up to tensof second) and be determined based on a difference measure betweencurrent performance of the plant (e.g., current distance to an object)and a target performance (e.g., a target distance to the object). Theperformance may be characterized by a performance function as describedin detail in co-owned and co-pending U.S. patent application Ser. No.13/487,499 entitled “STOCHASTIC APPARATUS AND METHODS FOR IMPLEMENTINGGENERALIZED LEARNING RULES, incorporated supra. Individual trials may beseparated in time (and in space) by practically any durationcommensurate with operational cycle of the plant. By way ofillustration, individual trial when training a robot to approach objectsand/or avoid obstacles may be separated by a time period and/or spacethat may be commensurate with the robot traversing from oneobject/obstacle to the next. In one or more implementations, the robotmay comprise a rover platform, and/or a robotic manipulator armcomprising one or more joints.

FIG. 6 illustrates an exemplary trajectory of a rover configured tolearn obstacle avoidance. The rover 610 may be configured to avoid walls602, 604. In some implementations, the avoidance policy may compriseexecution of a 45° turn, e.g., 606, 608 in FIG. 6. As used hereinaftersymbol 77V may be used to refer to a time of a given trial (e.g., T1denoting time of first trial). During first trial, at time T1:

-   -   the predictor (e.g., 202 of FIGS. 2A-2B) may receive control        output u1 (e.g., turn right) 45° from the controller 212. The        control signal may correspond to sensory input x1 (e.g., 206,        216 in FIG. 2A) that may be received by the controller and/or        the predictor; such signal may comprise a representation of an        obstacle (e.g., a wall), and/or a target (e.g., a charging        dock);    -   the predictor may be configured to generate predicted control        signal (e.g., u1^(P)=0°);    -   the combiner may produce the combined output u1′=45°; and    -   the plant 210 may begin to turn right in accordance with the        control signal (e.g., 224).

During another trial at time T2>T1:

-   -   the predictor 202 may receive control output u2 (e.g., still        turn right 45°) from the controller 212;    -   the plant feedback may indicate to the predictor that the plant        is executing a turn (in accordance with the prior combined        output u1′); accordingly, the predictor may be configured to        ‘mimic’ the prior combined output u1′ and to generate predicted        control signal (e.g., u2^(P)=10°);    -   the combiner may produce new combined output u2′=55°; and    -   the plant 210 may increase the turn rate in accordance with the        updated control signal u2′.

During another trial at time T3>T2:

-   -   the input x3 may indicate to the controller 212 that the plant        turn rate is in excess of the target turn rate for the 40° turn;        the controller 212 may reduce control output to u3=35°;    -   based on the input x3, indicative of e.g., the plant turn rate        for u2′=55°, the predictor may be configured to increase its        prediction to e.g., u3^(P)=20°; and    -   the combiner (e.g., 210 of FIG. 2A) may receive control output        u3 (e.g., turn right 35°) from the controller 212; the combiner        may produce the combined output u3′=55°.

During other trials at times Ti>T3 the predictor output may be increasedto the target plant turn of 45° and the controller output 208 may bereduced to zero. In some implementations, the outcome of the aboveoperational sequence may be referred to as (gradual) transfer of thecontroller output to the predictor output. A summary of oneimplementation of the training process described above may be summarizedusing data shown in Table 1:

TABLE 1 Control signal Predicted Combined Error Trial # u [deg] signalu^(P) [deg] signal ü [deg] (ü − u) [deg] 1 45 0 45 0 2 45 10 55 10 3 3520 55 10 4 25 35 60 15 5 25 50 60 15 6 0 55 55 10 7 0 55 55 10 8 −10 5545 0 9 −10 50 40 −5 10 0 45 45 0

As seen from Table 1, when the predictor is capable to producing thetarget output (e.g., trial #10), the controller output (e.g., 208 inFIG. 2A) may be withdrawn (removed). The output of the combiner (e.g.,214) in such realizations may comprise the predictor output inaccordance with, for example, Eqn. 14.

The controller (e.g., 212 in FIGS. 2A-2B and/or 2042 in FIG. 20B) maycomprise an adaptive system operable in accordance with a learningprocess. In one or more implementations, the learning process of thecontroller may comprise one or more reinforcement learning, unsupervisedlearning, supervised learning, and/or a combination thereof, asdescribed in co-owned and co-pending U.S. patent application Ser. No.13/487,499 entitled “STOCHASTIC APPARATUS AND METHODS FOR IMPLEMENTINGGENERALIZED LEARNING RULES, incorporated supra.

In one or more implementations, the training steps outlined above (e.g.,trials summarized in Table 1) may occur over two or more trials whereinindividual trial extend over behavioral time scales (e.g., one second totens of seconds).

In some implementations, the training steps may occur over two or moretrials wherein individual trials may be characterized by control updatescales (e.g., 1 ms to 1000 ms).

In some implementations, the operation of an adaptive predictor (e.g.,202 in FIG. 2A) may be characterized by predictor learning within agiven trial as illustrated and described with respect to Table 2.

FIG. 14 presents one exemplary configuration of training trials andcontrol update time scales. The plurality of vertical marks 1402 ontrace 1400 denotes control update events (e.g., time grid where controlcommands may be issued to motor controller). In some implementations(not shown) the control events (e.g., 1402 in FIG. 14) may be spaced atnon-regular intervals. The arrow denoted 1404 may refer to the controltime scale.

The time intervals denoted by brackets 1410, 1412, 1414 may refer toindividual training trials (e.g., trials Ti, T2, T3 described above withrespect to Table 1). The arrow denoted 1406 may refer to a trialduration being associated with, for example, a behavioral time scale.

The arrow denoted 1408 may refer to inter-trial intervals and describetraining time scale.

In some implementations, shown and described with respect to FIG. 14, arobotic device may be configured to learn two or more behaviors within agiven time span. By way of illustration, a mechanized robotic arm may betrained to grasp an object (e.g., cup). The cup grasping may becharacterized by two or more behaviors, e.g., B1 approach and B2 grasp.Training for individual behaviors B1, B2 is illustrated in FIG. 14 bytrials denoted as (1410, 1412, 1414), and (1420, 1422, 1424)respectively.

Sensory input associated with the training configuration of trace 1400is depicted by rectangles on trace 1430 in FIG. 14. Individual sensorystates (e.g., a particular object and or a feature present in thesensory input) are denoted as x1, x2 in FIG. 14. The cup may be presentin the sensory input associated with the trial Ti, denoted 1410 in FIG.14. Such predictor sensory input state may be denoted as x1. The roboticdevice may attempt to learn to approach (behavior B1) the cup at trial1410. The cup may be absent in the sensory input subsequent to trial1410. The robotic device may be engaged in learning other behaviorstriggered by other sensory stimuli. A different object (e.g., a bottle)denoted as x2 in FIG. 14 may be visible in the sensory input. Therobotic device may attempt to learn to grasp (behavior B2) the bottle attrial 1412. At a subsequent time, the cup may again be present in thesensory input. The robotic device may attempt to continue learning toapproach (behavior B1) the cup at trials 1412, 1414.

Whenever the bottle may be visible in the sensory input, the roboticdevice may continue learning grasping behavior (B2) trials 1422, 1424.In some realizations, learning trials of two or more behaviors mayoverlap in time (e.g., 1412, 1422 in FIG. 14). The robotic device may beconfigured to execute given actions (e.g., learn a behavior) in responseto a particular input stimuli rather than based on a particular time.

Operation of the controller (e.g., 212 in FIG. 2A) and/or the predictor(e.g., 202 in FIG. 2A) may be based on the input 206 (e.g., sensorycontext). As applied to the above illustration of training a rover toturn in response to, e.g., detecting an obstacle, as the rover executesthe turn the sensory input (e.g., the obstacle representation) maychange. Predictor training when the sensory input changes is describedbelow with respect to data summarized in Table 2, in accordance with oneor more implementations.

Responsive to the robotic controller detecting an obstacle (sensoryinput state x1), the controller output (e.g., 208 in FIG. 2A) maycomprise commands to execute a 45° turn. In some implementations, (e.g.,described with respect to Table 1 supra) the turn maneuver may comprisea sudden turn (e.g., executed in a single command, e.g., Turn=45°). Insome implementations, (e.g., described with respect to Table 2) the turnmaneuver may comprise a gradual turn effectuated by two or more turnincrements (e.g., executed in five commands, Turn=9°).

As shown in Table 2 during Trial 1, the controller output is configuredat 9° throughout the training. The sensory, associated with the turningrover, is considered as changing for individual turn steps. Individualturn steps (e.g., 1 through 5 in Table 2) are characterized by differentsensory input (state and/or context x1 through x5).

At presented in Table 2, during Trial 1, the predictor may be unable toadequately predict controller actions due to, at least in part,different input being associated with individual turn steps. The roveroperation during Trial 1 may be referred to as the controller controlledwith the controller performing 100% of the control.

TABLE 2 Trial 1 Trial 2 Trial 3 Step # State u° u^(P)° ü° u° u^(P)° ü°u° u^(P)° ü° 1 xl 9 0 9 9 3 12 5 6 11 2 x2 9 0 9 8 3 11 2 6 8 3 x3 9 0 97 3 10 3 5 8 4 x4 9 0 9 9 3 12 9 6 15 5 x5 9 0 9 3 3 6 1 5 6 Total 45 045 36 15 51 20 28 48

The Trial 2, summarized in Table 2, may correspond to another occurrenceof the object previously present in the sensory input processes atTrial 1. At step 1 of Trial 2, the controller output may comprise acommand to turn 9° based on appearance of the obstacle (e.g., x1) in thesensory input. Based on prior experience (e.g., associated with sensorystates x1 through x5 of Trail 1), the predictor may generate predictedoutput u^(P)=3° at steps 1 through 5 of Trial 2, as shown in Table 2. Inaccordance with sensory input and/or plant feedback, the controller mayvary control output u at steps 2 through 5. Overall, during Trial 2, thepredictor is able to contribute about 29% (e.g., 15° out of 51°) to theoverall control output u. The rover operation during Trial 2 may bereferred to as jointly controlled by the controller and the predictor.It is noteworthy, neither the predictor nor the controller are capableof individually providing target control output of 45° during Trial 2.

The Trial 3, summarized in Table 2, may correspond to another occurrenceof the object previously present in the sensory input processes atTrials 1 and 2. At step 1 of Trial 3, the controller output may reducecontrol output 3° turn based on the appearance of the obstacle (e.g.,x1) in the sensory input and/or prior experience during Trial 2, whereinthe combined output u1′ was in excess of the target 9°. Based on theprior experience (e.g., associated with sensory states x1 through x5 ofTrails 1 and 2), the predictor may generate predicted output u^(P)=5°,6° at steps 1 through 5 of Trial 3, as shown in Table 2. Variations inthe predictor output u^(P) during Trial 3 may be based on the respectivevariations of the controller output. In accordance with sensory inputand/or plant feedback, the controller may vary control output u at steps2 through 5. Overall, during Trial 3, the predictor is able tocontribute about 58% (e.g., 28° out of 48°) to the overall controloutput ü. The combined control output during Trial 3 is closer to thetarget output of 48°, compared to the combined output (51°) achieved atTrial 2. The rover operation during Trial 2 may be referred to asjointly controlled by the controller and the predictor. It isnoteworthy, the neither the predictor nor the controller are capable ofindividually providing target control output of 45° during Trial 3.

At a subsequent trial (not shown) the controller output may be reducedto zero while the predictor output may be increased to provide thetarget cumulative turn (e.g., 45°).

Training results shown and described with respect to Table 1-Table 2 arecharacterized by different sensory context (e.g., states x1 through x5)corresponding to individual training steps. Step-to-step sensory noveltymay prevent the predictor from learning controller output during theduration of the trial, as illustrated by constant predictor output u^(P)in the data of Table 1-Table 2.

Table 3 presents training results for an adaptive predictor apparatus(e.g., 202 of FIG. 2A) wherein a given state of the sensory may persistfor two or more steps during a trial, in accordance with one or moreimplementations. Persistence of the sensory input may enable thepredictor to learn controller output during the duration of the trial.

TABLE 3 Trial Step # State u° u^(P)° û° 1 x1 9 0 9 2 x1 9 3 12 3 x1 7 613 4 x2 9 0 9 5 x2 2 3 5 Total 36 12 48

As shown in Table 3, sensory state x1 may persist throughout thetraining steps 1 through 3 corresponding, for example, a view of a largeobject being present within field of view of sensor. The sensory statex2 may persist throughout the training steps 4 through 5 corresponding,for example, another view the large object being present sensed.

At steps 1,2 of Trial of Table 3, the controller may provide controloutput comprising a 9° turn control command. At step 3, the predictormay increase its output to 3°, based on a learned association betweenthe controller output u and the sensory state x1.

At step 3 of Trial of Table 3, the controller may reduce its output u to7° based on the combined output u2′=12° of the prior step exceeding thetarget output of 9°. The predictor may increase its output based ondetermining a discrepancy between the sensory state x1 and its prioroutput (3°).

At step 4 of Trial of Table 3, the sensory state (context) may change,due to for example a different portion of the object becoming visible.The predictor output may be reduced to zero as the new context x2 maynot have been previously observed.

At step 5 of Trial of Table 3, the controller may reduce its output u to2° based on determining amount of cumulative control output (e.g.,cumulative turn) achieved at steps 1 through 4. The predictor mayincrease its output from zero to 3° based on determining a discrepancybetween the sensory state x2 and its prior output u4^(P)=0°. Overall,during the Trial illustrated in Table 3, the predictor is able tocontribute about 25% (e.g., 12° out of 48°) to the overall controloutput ü.

FIG. 15 illustrates performance of training of a robotic apparatus,comprising an adaptive predictor of the disclosure. Solid line segments1502, 1504, 1506, 1508 denote predictor error obtained with trainingconfiguration wherein individual steps within a duration of a trial(denoted by arrow 1510) may be characterized by varying sensory state(e.g., states x1 through x5 described with respect to Table 2). Brokenline segments 1512, 1514, 1516, 1518 denote predictor error obtainedwith training configuration wherein individual steps within the trialduration may be characterized by consistent sensory input state (e.g.,states x1 through x5 described with respect to Table 2). In someimplementations, the predictor error may be described as follows:ε(t _(i))=|u ^(P)(t _(i−1))−u ^(d)(t _(i))|.  (Eqn. 15)In other words, prediction error is determined based on (how well) theprior predictor output matches the current teaching (e.g., target)input. In one or more implementations, predictor error may comprise aroot-mean-square deviation (RMSD), coefficient of variation, and/orother parameters.

As shown in FIG. 15, predictor error diminishes as training progresses(e.g., with increasing trial number). It is noteworthy that: (i)predictor error corresponding to segments 1502, 1504, 1506, 1508 (e.g.,constant sensory state during trials) may be constant (and/or varyinginsignificantly) through individual trials; and (ii) predictor errorcorresponding to segments 1512, 1514, 1516, 1518 (e.g., constant sensorystate during trials) may diminish through individual trials. The latterbehavior may be related to a greater degree of sustained sensoryexperience by the predictor during learning responsive to consistentsensory input.

Referring now to FIG. 2B, predictor operation (e.g., 222 in FIG. 2B)based on a training signal is discussed, in accordance with one or moreimplementations. The predictor 222 of the adaptive control system 220 ofFIG. 2B may comprise a spiking neuron network (comprising for examplethe neuron 140 of FIG. 1B above) configured to implement reinforcementand/or supervised learning described with respect to FIG. 1B above.

The training signal (e.g., 204 in FIG. 2B) may be configured to informthe predictor about the combined output to the plant. This configurationmay enable the predictor 222 to adjust the predicted output 238 to matchthe target output 240 more rapidly, compared to the predictor output inthe absence of the training signal (e.g., output 218 of FIG. 2A).

Some existing adaptive controllers avoid using controller output as theteaching input into the same system, as any output drift and/or anerroneous output may be reinforced via learning, resulting in a drift,e.g., growing errors with time, in the outputs of the learning system.

Control configuration (e.g., such as illustrated in FIG. 2B) whereinoutput of the predictor may be fed back to the predictor as a teachingsignal, may advantageously reduce susceptibility of the control system(e.g., 220 in FIG. 2B) to drift and/or DC bias. For example, responsiveto absence of controller output 208, the teaching signal may comprise acopy of the predictor output. In some implementations, (e.g., of Eqn.15) responsive to the predictor output matching the target signal,predictor state may remain unchanged (e.g., no adaptation) due to zeroerror. In one or more implementations characterized by the predictedsignal 238 drift, the controller may generate a correction signal. Thecontroller output (e.g., 208) may be combined with the predictor output238 to generate the teaching signal 204 thereby removing the drift.

The combiner 234 may be operated in accordance with the transferfunction expressed, for example via Eqn. 7.

An exemplary training sequence of adaptive system 220 operation,comprising the predictor training input 204 of FIG. 2B may be expressedas follows:

During first trial at time T1:

-   -   the controller may receive a sensory input (e.g., 206, 226 in        FIG. 2B) containing x1 and may generate output u1;    -   the predictor may receive the sensory input x1 (or a portion of        thereof), and may be configured to generate predicted control        signal (e.g., u1^(P)=0°);    -   the combiner may produce the combined output 1.11=45°; this        output may be provided to the predictor as the teaching (target)        signal at a subsequent time instance; and    -   the plant 210 may begin to turn right in accordance with the        combined control signal (e.g., 240)/11=45°.

During another trial at time T2>T1:

-   -   the controller may receive a sensory input (e.g., 206, 226 in        FIG. 2B) containing x1 and may generate output u2=45°;    -   the predictor may receive the sensory input x1 (or a portion of        thereof), and the teaching (target) signal ü1=45° produced by        the combiner at a prior trial (e.g., T1); the predictor may be        configured to ‘mimic’ the combined output ft; the predictor may        be configured to generate predicted control signal (e.g.,        u2^(P)=30°) based on the sensory input, plant feedback and/or        the teaching signal;    -   the combiner may produce the combined output ü2=75° (e.g., in        accordance with, for example, Eqn. 7); and    -   the plant 210 may increase the turn rate with the control signal        ü2.

During another trial at time T3>T2:

-   -   the controller may determine that the rate of turn is in excess        of the target turn of 45°, and may generate control output        u3=0°;    -   the predictor may receive the sensory input x (or a portion of        thereof), and the teaching (target) signal ü2=75° produced by        the combiner at a prior trial (e.g., T2); the predictor may be        configured to generate predicted control signal (e.g., u3P=50°)        based on the sensory input, plant feedback and/or the teaching        signal;    -   the combiner may produce the combined output/13=50° (e.g., in        accordance with, for example, Eqn. 7); and    -   the plant 210 may execute the turn in accordance with the        control signal ü3.

Subsequently, at times T4, T5, TM>T2 the predictor output to thecombiner 234 may result in the control signal 240 to turn the plant by45° and the controller output 208 may be reduced to zero. In someimplementations, the outcome of the above operational sequence may bereferred to as (gradual) transfer of the controller output to thepredictor output. When the predictor is capable to producing the targetoutput, the controller output (e.g., 208 in FIGS. 2A-2B) may bewithdrawn (removed). The output of the combiner (e.g., 214, 234) maycomprise the predictor output in accordance with, for example, Eqn. 3.

In one or more implementations comprising spiking control and/orpredictor signals (e.g., 208, 218, 238, 224, 240 in FIG. 2A-2B), thewithdrawal of the controller output may correspond to the controller 208generating spike output at a base (background) rate. By way ofillustration, spike output at a (background) rate of 2 Hz may correspondto ‘maintain course’ control output; output above 2 Hz may indicate aturn command. The turn rate may be encoded as spike rate, number ofspikes, and/or spike latency in various implementations. In someimplementations, zero output (e.g., controller 208, predictor 218,and/or combiner 240) may comprise a ‘zero signal’, such as a pre-definedsignal, a constant (e.g., a dc offset or a bias), spiking activity at amean-firing rate, and/or other zero signal.

Output 224, 240 of the combiner e.g., 214, 234, respectively, may begated. In some implementations, the gating information may be providedto the combiner by the controller 212. In one such realization ofspiking controller output, the controller signal 208 may comprisepositive spikes indicative of a control command and configured to becombined with the predictor output (e.g., 218); the controller signal208 may comprise negative spikes, where the timing of the negativespikes is configured to communicate the control command, and the(negative) amplitude sign is configured to communicate the combinationinhibition information to the combiner 214 so as to enable the combinerto ‘ignore’ the predictor output 218 for constructing the combinedoutput 224.

In some implementations of spiking controller output, the combiner 214,234 may comprise a spiking neuron (e.g., the neuron 140 described inFIG. 1B supra); and the controller signal 208 may be communicated viatwo or more connections. One such connection may be configured tocommunicate spikes indicative of a control command to the combinerneuron (e.g., via connection 124 in FIG. 1B); the other connection maybe used to communicate an inhibitory signal to the combiner neuron(e.g., via connection 134 in FIG. 1B). The inhibitory signal may causethe combiner 214, 234 to ‘ignore’ the predictor output 218 whenconstructing the combined output 224.

The gating information may be provided to the combiner via a connection242 from another entity (e.g., a human operator controlling the systemwith a remote control, and/or external controller) and/or from anotheroutput from the controller 212 (e.g. an adapting block, or an optimalcontroller). In one or more implementations, the gating informationdelivered via the connection 242 may comprise any of a command, a memoryaddress of a register storing a flag, a message, an inhibitory efficacy,a value (e.g., a weight of zero to be applied to the predictor output218 by the combiner), and/or other information capable of conveyinggating instructions to the combiner.

The gating information may be used by the combiner e.g., 214, 234 toinhibit and/or suppress the transfer function operation. The suppression‘veto’ may cause the combiner output (e.g., 224, 240) to be comprised ofthe controller portion 208, e.g., in accordance with Eqn. 4. In someimplementations, the combiner (e.g., 234 in FIG. 2B) may comprise anetwork of a spiking neurons. One or more neurons of the combinernetwork may be configured to receive the predicted input (e.g., 238 inFIG. 2B), referred to as the combiner input neurons. The gating signalmay comprise an inhibitory indication (delivered to the combiner networkby, e.g., the connection 242) that may be configured to inhibitindividual ones of the one or more combiner input neurons of thecombiner network thereby effectively removing the predictor input fromthe combined output (e.g., 240 in FIG. 2B).

In one or more implementations, another one or more neurons of thecombiner network may be configured to generate the combined output(e.g., 240 in FIG. 2B), referred to as the combiner output neurons. Thegating signal may comprise an inhibitory indication (delivered to thecombiner network by, e.g., the connection 242) that may be configured toinhibit the output from the combiner. Zero combiner output may, in somerealizations, cause zero teaching signal (e.g., 204 in FIG. 2B) to beprovided to the predictor so as to signal to the predictor a discrepancybetween the target action (e.g., controller output 208) and thepredicted action (e.g., output 238).

The gating signal may be used to veto predictor output 218, 238 based onthe predicted output being away from the target output by more than agiven margin. The margin may be configured based on an applicationand/or state of the trajectory. For example, a smaller margin may beapplicable in navigation applications wherein the platform is proximateto a hazard (e.g., a cliff) and/or an obstacle. A larger error may betolerated when approaching one (of many) targets.

By way of a non-limiting illustration, if the turn is to be completedand/or aborted (due to, for example, a trajectory change and/or sensoryinput change), and the predictor output may still be producing turninstruction to the plant, the gating signal may cause the combiner toveto (ignore) the predictor contribution and to pass through thecontroller contribution.

Predictor and controller outputs (218/228, 208, respectively in FIGS.2A-2B) may be of opposite signs. In one or more implementations,positive predictor output (e.g., 228) may exceed the target output thatmay be appropriate for performance of as task (e.g., as illustrated bydata of trials 8-9 in Table 1). Controller output may be configured tocomprise negative signal (e.g., −10) in order to compensate foroverprediction by the predictor.

Gating and/or sign reversal of controller output, may be useful, forexample, responsive to the predictor output being incompatible with thesensory input (e.g., navigating towards a wrong target). Rapid (comparedto the predictor learning time scale) changes in the environment (e.g.,appearance of a new obstacle, target disappearance), may require acapability by the controller (and/or supervisor) to ‘overwrite’predictor output. In one or more implementations compensation foroverprediction may be controlled by a graded form of the gating signaldelivered via the connection 242.

FIGS. 3A-3B illustrate combiner apparatus configured to operate withmultichannel controller and/or predictor signals (e.g., 208, 218/238 inFIGS. 2A-2B, respectively). The combiner apparatus may be utilized withan adaptive controller (e.g., the controller 102 in FIG. 1A and/or thecontroller 200, 220, of FIGS. 2A-2B) configured to operate a plant(e.g., the plant 110, in FIG. 1A and/or the plant 210 in FIGS. 2A-2B).

The combiner 310 of FIG. 3A may receive an M-dimensional (M>1) controlsignal 308. The control signal U may comprise a vector corresponding toa plurality of input channels (e.g., 3081, 3082 in FIG. 3A). Individualchannels, may be configured to communicate individual dimensions (e.g.,vectors) of the input U, as described in detail for example with respectto FIG. 13B, below.

The predictor may be configured to generate N-dimensional (N>1)predicted control output 318. In some implementations, individualteaching signal may be de-multiplexed into multiple teaching components.Predictor learning process may be configured to adapt predictor statebased on an individual teaching component. In one or moreimplementations, predictor may comprise two or more learning processes(effectuated, for example, by a respective neuron network) whereinadaptation of individual learning process may be based on a respectiveteaching input component (or components). In one or moreimplementations, multichannel predictor may be comprises of an array ofindividual predictors as in 222

The predicted signal U^(P) may comprise a vector corresponding to aplurality of output channels (e.g., 3181, 3182 in FIG. 3A). Individualchannels 3181, 3182 may be configured to communicate individualdimensions (e.g., vectors) of the signal 318, as described in detail forexample with respect to FIG. 13B, below. Predictor 302 operation may bebased on one or more sensory inputs 306. In some implementations, thesensory input 306 may comprise the sensory input 106 and/or 206,described with respect to FIGS. 1A, 2B above.

The combiner may be operable in accordance with a transfer function hconfigured to combine signals 308, 318 and to produce single-dimensionalcontrol output 320:u=h(U,U ^(P)).  (Eqn. 16)In one or more implementations, the combined control output 320 may beprovided to the predictor as the training signal. The training signalmay be utilized by the predictor learning process in order to generatethe predicted output 318 (e.g., as described with respect to FIG. 2B,supra).

The use of multiple input channels (3081, 3082 in FIG. 3B) and/ormultiple predictor output channels (e.g., 3381, 338_2 in FIG. 3B) tocommunicate a single signal (e.g., control signal U and/or predictedcontrol signal U^(P)) may enable more robust data transmission whencompared to a single channel per signal data transmission schemes.Multichannel data transmission may be advantageous in the presence ofbackground noise and/or interference on one or more channels. In someimplementations, wherein individual channels are characterized bybandwidth that may be lower than the data rate requirements for thetransmitted signal (e.g., control signal U and/or predicted controlsignal U^(P)) multichannel data transmission may be utilized tode-multiplex the higher data rate signal onto two or more lower capacitycommunications channels (e.g., 3381, 338_2 in FIG. 3B).

FIG. 3B illustrates an adaptive system 331 wherein the predictor 332 isconfigured to receive the combined control output (e.g., 340 in FIG. 3B)as a part of the sensory input 336 (e.g., 306 in FIG. 3A). TheN-dimensional output 338 of the predictor 332 may be combined by thecombiner 342 with the M-dimensional control input 308 to produce controlsignal 340. In one or more implementations the combined control signal340 may serve as input to another predictor configured to receive thisinput (as e.g., 216, 306 in FIGS. 2B-3A).

The N-dimensional predictor output 338 may comprise a plurality ofvectors corresponding to a plurality of channels (e.g., 3381, 338_2 inFIG. 3B). Individual channels 3381, 338_2 may be configured tocommunicate individual dimensions (e.g., vectors) of the predictedsignal U^(P), as described in detail for example with respect to FIG.13B, below.

The predictor 332 learning process may be operable based on a teachingsignal 334. Mapping between the controller input 308, the predictoroutput 338, the combiner output 340, and the teaching signal 334 maycomprise various signal mapping schemes. In some implementations,mapping schemes may include one to many, many to one, some to some, manyto some, and/or other schemes.

In spiking neuron networks implementations, inputs (e.g., 308, 318 and308 337 of FIGS. 3A-3B, respectively) into a combiner (e.g., 310, 342 inFIGS. 3A-3B) may comprise signals encoded using spike latency and/orspike rate. In some implementations, inputs into the combiner may beencoded using one encoding mechanism (e.g., rate). In one or moreimplementations, inputs into the combiner may be encoded using singletwo (or more) encoding mechanisms (e.g., rate, latency, and/or other).

In some implementations, connections delivering inputs into one or morespiking neurons of the combiner (e.g., connections 124 in FIG. 1B) maybe configured to directly modify one or more parameters of the, inputsinto the combiner may be encoded using one encoding mechanism (e.g.,rate). In one or more implementations, inputs into the combiner may beencoded using single two (or more) encoding mechanisms (e.g., rate,latency, and/or other encoding mechanisms).

Combiner output (320, 340) may be encoded using spike latency and/orspike rate. In some implementations, the output encoding type may matchthe input encoding type (e.g., latency in—latency out). In someimplementations, the output encoding type may differ from the inputencoding type (e.g., latency in—rate out).

Combiner operation, comprising input decoding-output encodingmethodology, may be based on an implicit output determination. In someimplementations, the implicit output determination may comprise,determining one or more input values using latency and/or rate inputconversion into e.g., floating point and/or integer; updating neurondynamic process based on the one or more input values; and encodingneuron output into rate or latency. In one or more implementations, theneuron process may comprise a deterministic realization (e.g.,Izhikevich neuron model, described for example in co-owned U.S. patentapplication Ser. No. 13/623,842, entitled “SPIKING NEURON NETWORKADAPTIVE CONTROL APPARATUS AND METHODS”, filed Sep. 20, 2012,incorporated supra; and/or a stochastic process such as described, forexample, in U.S. patent application Ser. No. 13/487,533, entitled“STOCHASTIC SPIKING NETWORK LEARNING APPARATUS AND METHODS”,incorporated supra.

In some implementations, combiner operation, comprising inputdecoding-output encoding methodology, may be based on an explicit outputdetermination, such as, for example, expressed by Eqn. 4-Eqn. 9, Eqn.14, Eqn. 20-Eqn. 21. FIG. 3C illustrates an adaptive system 350comprising a predictor 352 and two or more combiner apparatus 359.Individual combiner apparatus (e.g., 3591), of system 530 may beoperable in accordance with any of the applicable methodologies, such asdescribed above with respect to FIG. 3B.

The predictor 352 may be configured to generate predicted output 358based on the sensory input 356 (e.g., 306 in FIG. 3A), and two or moreteaching signals 354 that may comprise outputs 360 of the respectivecombiners 359. Combiner output (e.g., 360_1) may be determined based onthe controller input (e.g., 308) and the respective predictor outputusing, for example, Eqn. 7. In some implementations, input into thecombiners 359 may comprise multichannel input, as described, forexample, with respect to FIG. 3B, above.

In some implementations, a complex teaching signal may be decomposedinto multiple components that may drive adaptation of multiple predictorblocks (associated with individual output channels, e.g., 388 in FIG.3D). Prediction of a (given) teaching signal 354 may be spread overmultiple predictor output channels 358. Once adapted, outputs ofmultiple predictor blocks may be combined thereby providing predictionof the teaching signal (e.g., 354 in FIG. 3C). Such an implementationmay increase the number of teaching signals that can be mediated using afinite set of control signal channels.

In one or more implementations, a single output predictor channel 358may contain prediction of multiple teaching signals (e.g., 374 in FIG.3D). That approach may be utilized responsive to information capacity ofthe predictor output channel (e.g., how much information may be encodedonto a single channel) is higher than information capacity of teachingsignal.

In some implementations, a combination of the above approaches (e.g.,comprising two or more teaching signals and two or more predictor outputchannels) may be employed.

FIG. 3D illustrates an adaptive system 370 comprising a multiplexingpredictor 372 and two or more combiner apparatus 379. Controller input Umay be de-multiplexed into two (e.g., input 378_1 into combiners 3791,379_2) and/or more (input 378_2 into combiners 3791, 3792, 3793).Individual combiner apparatus 379 may be configured to multiplex one (ormore) controller inputs 378 and two or more predictor outputs U^(P) 388to form a combined signal 380. In some implementations, the predictoroutput for a given combiner may be spread (de-multiplexed) over multipleprediction channels (e.g., 3881, 388_2 for combiner 379_2). In one ormore implementations, teaching input to a predictor may be delivered viamultiple teaching signal 374 associated with two or more combiners.

The predictor 372 may operate in accordance with a learning processconfigured to determine an input-output transformation such that theoutput of the predictor U^(P) after learning is configured to match theoutput of the combiner h(U, U^(P)) prior to learning (e.g., when U^(P)comprises a null signal).

Predictor transformation F may be expressed as follows:U ^(P) −F(Û),Û=h(U ^(P)).  (Eqn. 17)

In some implementations, wherein dimensionality of control signal Umatches dimensionality of predictor output U^(P), the transformation ofEqn. 17 may be expressed in matrix form as:U ^(P) =FÛ,Û=HU ^(P) ,F=inv(H).  (Eqn. 18)where H may denote the combiner transfer matrix composed of transfervectors for individual combiners 379 H=[h1, h2, . . . , hn], Ü=[111,112, . . . ûn] may denote output matrix composed of output vectors 380of individual combiners; and F may denote the predictor transformmatrix. The combiner output 380 may be provided to the predictor 372and/or another predictor apparatus as teaching signal 374 in FIG. 3D. Insome implementations, (e.g., shown in FIG. 3B), the combiner output 380may be provided to the predictor 372 as sensory input signal (not shownin FIG. 3D).

In some implementations of multi-channel predictor (e.g., 352, 372)and/or combiner (e.g., 35, 379) various signal mapping relationships maybe utilized such as, for example, one to many, many to one, some tosome, many to some, and/or other relationships (e.g., one to one).

In some implementations, prediction of an individual teaching signal(e.g., 304 in FIG. 3A) may be spread over multiple prediction channels(e.g., 318 in FIG. 3A). In one or more implementations, an individualpredictor output channel (e.g., 388_2 in FIG. 3D) may contain predictionof multiple teaching signals (e.g., two or more channels 374 in FIG.3D).

Transfer function h (and or transfer matrix H) of the combiner (e.g.,359, 379 in FIGS. 3C-3D) may be configured to perform a state spacetransformation of the control input (e.g., 308, 378 in FIGS. 3C-3D)and/or predicted signal (e.g., 358, 388 in FIGS. 3C-3D). In one or moreimplementations, the transformation may comprise one or more of atime-domain to frequency domain transformations (e.g., Fouriertransform, discrete Fourier transform, discrete cosine transform,wavelet and/or other transformations), frequency domain to time domaintransformations (e.g., inverse Fourier transform, inverse discreteFourier transform, inverse discrete cosine transform, and/or othertransformations), wavenumber transform, and/or other transformations.The state space transformation may comprise an application of a functionto one (or both) input parameters (e.g., u, u^(P)) into the combiner. Insome implementations, the function may be selected from an exponentialfunction, logarithm function, a Heaviside step function, and/or otherfunctions.

In implementations where the combiner is configured to perform thestate—space transform (e.g., time-space to frequency space), thepredictor may be configured to learn an inverse of that transform (e.g.,frequency-space to time-space). Such predictor may be capable oflearning to transform, for example, frequency-space input a intotime-space output u^(P).

In some implementations, predictor learning process may be configuredbased on one or more look-up tables (LUT). Table 4 and Table 5illustrate use of look up tables for learning obstacle avoidancebehavior (e.g., as described with respect to Table 1-Table 3 and/or FIG.7, below).

Table 4-Table 5 present exemplary LUT realizations characterizing therelationship between sensory input (e.g., distance to obstacle d) andcontrol output (e.g., turn angle a relative to current course) obtainedby the predictor during training. Columns labeled N in Table 4-Table 5,present use occurrence N (i.e., how many times a given control actionhas been selected for a given input, e.g., distance). Responsive to aselection of a given control action (e.g., turn of 15°) based on thesensory input (e.g., distance from an obstacle of 0.7 m), the counter Nfor that action may be incremented. In some implementations of learningcomprising opposing control actions (e.g., right and left turns shown byrows 3-4 in Table 5), responsive to a selection of one action (e.g.,turn of +15°) during learning, the counter N for that action may beincremented while the counter for the opposing action may bedecremented.

As seen from the example shown in Table 4, as a function of the distanceto obstacle falling to a given level (e.g., 0.7 m), the controller mayproduce a turn command. A 15° turn is most frequently selected duringtraining for distance to obstacle of 0.7 m. In some implementations,predictor may be configured to store the LUT (e.g., Table 4) data foruse during subsequent operation. During operation, the most frequentlyused response (e.g., turn of 15°) may be output for a given sensoryinput, in one or more implementations, In some implementations, thepredictor may output an average of stored responses (e.g., an average ofrows 3-5 in Table 4).

TABLE 4 d a° N 0.9 0 10 0.8 0 10 0.7 15 12 0.7 10 4 0.7 5 1 0.5 45 3

TABLE 5 d a° N 0.9 0 10 0.8 0 10 0.7 15 12 0.7 −15 4 0.5 45 3

In one or more implementations (e.g., the predictor 400 of FIG. 4)wherein the predictor comprises a spiking neuron (e.g., 140 of FIG. 1B)network, learning a given behavior (e.g., obstacle avoidance and/ortarget approach) may be effectuated by storing an array of efficacies(e.g., 126 in FIG. 1B) of connections within the predictor network. Insome implementations, the efficacies may comprise connection weights,adjusted during learning using, for example, methodology of Eqn. 11,described above. In some implementations, connection plasticity (e.g.,efficacy adjustment of Eqn. 11) may be implemented based on the teachinginput as follows:

-   -   based on a teaching input (e.g., spike) and absence of neuron        output spike connections delivering input spikes into the neuron        (active connection) that precede the teaching spike (within a        plasticity window), may be potentiated; and/or    -   based on neuron output spike in absence of teaching input,        active connections delivering input spikes into the neuron        (active connection)) that precede the output spike (within a        duration specified by plasticity window), may be depressed.        In some implementations wherein the sensory input may be updated        at 40 ms intervals and/or control signal may be updated at a        rate of 1-1000 Hz, the duration of the plasticity window may be        selected between 1 ms and 1000 ms. Upon learning a behavior,        network configuration (e.g., an array of weights) may be stored        for future use by the predictor.

FIG. 20A illustrates an adaptive predictor configured to develop anassociation between a control action and sensory context, according toone or more implementations. The control system 2000 of FIG. 20A maycomprise an adaptive predictor 2002 and a combiner 2012. The combiner2012 may receive an action indication 2008 from a controller apparatus(e.g., the apparatus 2042 of FIG. 20B and/or 212 of FIG. 2B).

In some implementations of a control system, such as described withrespect to FIGS. 20A-20B, the controller (e.g., 2042 in FIG. 20B) may beconfigured to issue a higher level control directive, e.g., the actionindication 2008, 2048, in FIGS. 20A-20B that are not directlycommunicated to the plant (2040) but rather are directed to thepredictor (e.g., 2002, 2022 in FIGS. 20A-20B). As used herein the term“action indication” may be used to refer to a higher level instructionby the controller that is not directly communicated to the plant. In oneor more implementations, the action indication may comprise, forexample, a directive ‘turn’, ‘move ahead’. In some implementations, thecontrol system may utilize a hierarchy of action indications, rangingfrom less complex/more specific (e.g., turn, move) to more abstract:approach, avoid, fetch, park, grab, and/or other instructions.

Action indications (e.g., 2008, 2048 in FIGS. 20A-20B) may be configuredbased on sensory context (e.g., the sensory input 2006, 2026 in FIGS.20A-20B). In one or more implementations, the context may correspond topresence of an object (e.g., a target and/or an obstacle), and/or objectparameters (e.g., location), as illustrated in FIG. 12. Panels 1200,1210 in FIG. 12 illustrate position of a robotic device 1202, comprisingfor example, a control system (e.g., 2000, 2020 of FIGS. 20A-20B). Thecontrol system may comprise a controller (2042) and a predictor (2002,2022). The device 2012 may be configured to approach a target 1208,1218. The controller of the device 2012 may provide an action indicationA1 1204, A2 1214 that may be configured in accordance with the sensorycontext, e.g., the location of the object 1208, 1218 with respect to thedevice 1202. By way of a non-limiting example, responsive to the object1208 being to the right of the device 1202 trajectory (as shown in thepanel 1200), the action indication A1 1204 may indicate an instruction“turn right”. Responsive to the object 1208 being to the left of thedevice 1202 trajectory (as shown in the panel 1210), the actionindication A2 1214 may indicate an instruction “turn left”. Responsiveto the sensory context and the instruction 1204, the predictor of thedevice control system may generate low level motor commands (e.g.,depicted by broken arrows 1206, 1216 in FIG. 12) to execute therespective 90° turns to right/left.

Returning now to FIG. 20A, the control system 2000 may comprise thecombiner 2012 configured to receive the controller action indication A2008 and predicted action indication 2018. In some implementations, thecombiner 2012 may be operable in accordance with any applicablemethodologies, e.g., described with respect to FIGS. 2A-3C, above.

The predictor 2002 may be configured to generate the predicted actionindication A^(P) 2018 based on the sensory context 2006 and/or trainingsignal 2004. In some implementations, the training signal 2004 maycomprise the combined output A.

In one or more implementations, generation of the predicted actionindication 2018 may be based on the combined signal A being provided asa part of the sensory input (2016) to the predictor. In someimplementations comprising the feedback loop 2018, 2012, 2016 in FIG.20A, the predictor output 2018 may be combined with the controlleraction indication 2008. The combiner 2010 output signal 2012 may be usedas an input 2016 to the predictor. Predictor realizations, comprisingaction indication feedback connectivity, may be employed inapplications, wherein (i) the action indication may comprise a sequenceof timed actions (e.g., hitting a stationary target (e.g., a nail) witha hammer by a robotic arm); (ii) the predictor learning process may besensory driven (e.g., by the sensory input 2006) in absence of plantfeedback (e.g., 2036 in FIG. 20B); and (iii) the plant may becharacterized by a plant state time parameter that may be greater thanthe rate of action updates (e.g., the sequence of timed actions 2008).It may be advantageous for the predictor 2002 to account for a prioraction within the sequence (e.g., via the feedback input 2016) so as totake into account the effect of its previous state and/or previouspredictions in order to make a new prediction. Such methodology may beof use in predicting a sequence of actions/behaviors at time scaleswhere the previous actions and/or behavior may affect the nextactions/behavior, e.g., when a robotic manipulator arm may be tasked tofill with coffee three cups one a tray: completion of the first action(filling one cup) may leave the plant (arm) closes to another cup ratherthan the starting location (e.g., coffee machine). The use of feedbackmay reduce coffee serving time by enabling the arm to move from one cupto another without returning to the coffee machine in between. In someimplementations (not shown), action feedback (e.g., 2016 in FIG. 20A)may be provided to other predictors configured to predict control inputfor other tasks (e.g., filling the coffee pot with water, and/or placingthe pot into the coffee maker).

In some implementations, generation of the predicted action indicationA^(P) by the predictor 2002 may be effectuated using any of theapplicable methodologies described above (e.g., with respect to FIGS.2A-3C). The predictor may utilize a learning process based on theteaching signal 2004 in order to associate action indication A with thesensory input 2006 and/or 2016.

The predictor 2002 may be further configured to generate the plantcontrol signal 2014 low level control commands/instructions based on thesensory context 2006. The predicted control output 2014 may beinterfaced to a plant. In some control implementations, such low-levelcommands may comprise instructions to rotate a right wheel motor by 30°,apply motor current of 100 mA, set motor torque to 10%, reduce lensdiaphragm setting by 2, and/or other commands. The low-level commandsmay be configured in accordance with a specific implementation of theplant, e.g., number of wheels, motor current draw settings, diaphragmsetting range, gear ration range, and/or other parameters.

In some implementations of target approach, such as illustrated in FIG.12, a predictor may be configured to learn different behaviors (e.g.,generate different motor output commands) based on the received sensorycontext. Responsive to: (i) a target appearing on right/left (withrespect to the robotic plant); and (ii) ‘turn’ action indication thepredictor may learn to associate a turn towards the target (e.g.,right/left turn 1206, 1216 in FIG. 12). The actual configuration of theturn commands, e.g., rate of turn, timing, turn angle, may be configuredby the predictor based on the plant state (platform speed, wheelposition, motor torque) and/or sensory input (e.g., distance to target,target size) independent of the controller.

Responsive to the ‘turn’ command arriving to the predictor proximate intime to the sensory context indicative of a target, the predictor maygenerate right/left turn control signal in the presence of the sensorycontext. Time proximity may be configured based on a particularapplication parameters (e.g., robot speed, terrain, object/obstaclesize, location distance, and/or other parameters). In some applicationsto garbage collecting robot, the turn command may be time locked (towithin +10 ms) from the sensory context indicative of a need to turn(for example toward a target). In some realizations, a target appearingto the right of the robot in absence of obstacles may trigger the action‘turn right’.

During learning predictor may associate movement towards the target(behavior) with the action indication. Subsequently during operation,the predictor may execute the behavior (e.g., turn toward the target)based on a receipt of the action indication (e.g., the ‘turn’instruction). In one or more implementations, the predictor may beconfigured to not generate control output (e.g., 2014 in FIG. 20A) inabsence of the action tag (e.g., 2008 and/or 2004). In other words, thepredictor may learn to execute the expected (learned) behaviorresponsive to the presence of the action indication (informing whataction to perform, e.g., turn) and the sensory input (informing thepredictor where to turn).

Such associations between the sensory input and the action indicator mayform a plurality of composite motor primitive comprising an actionindication (e.g., A=turn) and actual control instructions to the plantthat may be configured in accordance with the plant state and sensoryinput.

In some implementations, the predictor may be configured to learn theaction indication (e.g., the signal 2008 in FIG. 20B) based on the priorassociations between the sensory input and the action tag. The predictormay utilize learning history corresponding to a sensory state (e.g.,sensory state x1 described with respect to Table 3) and the occurrenceof the action tag contemporaneous to the sensory context x1. By way ofillustration, based on two or more occurrences (prior to time t1) of thetag A=‘turn’ temporally proximate to a target object (e.g., 1208, 1218in FIG. 12) being present to one side from the robotic device 1202, thepredictor may (at time t1) generate the tag (e.g., signal 2018) inabsence of the tag input 2008.

Based on learning of associations between action tag-control command;and/or learning to generate action tags, the predictor may be able tolearn higher-order control composites, such as, for example, ‘approach’,‘fetch’, ‘avoid’, and/or other actions, that may be associated with thesensory input.

FIG. 20B is a block diagram illustrating an control system comprising anadaptive predictor configured to generate control signal based on anassociation between control action and sensory context, according to oneor more implementations.

The control system 2020 may comprise controller 2042, predictor 2022,plant 2040, and one or more combiners 2030, 2050. The controller 2042may be configured to generate action indication A 2048 based on sensoryinput 2026 and/or plant feedback 2036. The controller 2042 may befurther configured to generate one or more low-level plant controlcommands (e.g., 2046) based on sensory input 2026 and/or plant feedback2036. In some control implementations, the low-level commands 2046 maycomprise instructions to rotate a right wheel motor by 30°, apply motorcurrent of 100 mA, set motor torque to 10%, reduce lens diaphragmsetting by 2, and/or other commands. The low-level commands may beconfigured in accordance with a specific implementation of the plant,e.g., number of wheels, motor current draw settings, diaphragm settingrange, gear ration range, and/or other parameters.

One or more of the combiners of the control system of FIG. 20B (e.g.,2030_1) may be configured to combine an action indication (e.g., tag A)2048_1, provided by the controller, and the predicted action tag A^(P)from the predictor to produce a target action tag A 20321.

One or more of the combiners (e.g., 2050) may be configured to combine acontrol command 2046, provided by the controller, and the predictedcontrol instructions u^(P) 2044, provided by the predictor, to produceplant control instructions ü=h(u,u′) (e.g., 2052).

The predictor 2022 may be configured to perform prediction of (i) one ormore action indications 2048; and/or plant control output u^(P) 2052that may be associated with the sensory input 2026 and/or plant feedback2036. The predictor 2022 operation may be configured based on two ormore training signals 2024, 2054 that may be associated with the actionindication prediction and control command prediction, respectively. Inone or more implementations, the training signals 2024, 2054 at time t2may comprise outputs of the respective combiners 2030, 2050 at a priortime (e.g., t1=t2−dt), as described above with respect to Eqn. 7.

The predictor 2022 may be operable in accordance with a learning processconfigured to enable the predictor to develop associations between theaction indication input (e.g., 2048_1) and the lower-level controlsignal (e.g., 2052). In some implementations, during learning, thisassociation development may be aided by plant control instructions(e.g., 2046) that may be issued by the controller 2042. One (or both) ofthe combined action indication signal (e.g., 2032_1) and/or the combinedcontrol signal (e.g., 2052) may be utilized as a training input (denotedin FIG. 20B by the arrows 20241, 2054, respectively) by the predictorlearning process. Subsequent to learning, once the predictor hasassociated the action indicator with the sensory context, the low-levelcontrol output (e.g., 2046) may be withdrawn by the controller.Accordingly, the control system 2020 of FIG. 20B may take configurationof the control system 2000 shown in FIG. 20A.

In some implementations, the combined action indication signal (e.g.,2032) and/or the combined control signal (e.g., 2052) may be provided tothe predictor as a portion of the sensory input, denoted by the arrows2056 in FIG. 20B.

In one or more implementations, two or more action indications (e.g.,2048_1, 2048_2 may be associated with the control signal 2052. By way ofa non-limiting example, illustrated for example in FIG. 21, thecontroller apparatus 2020 may be configured to operate a roboticplatform. Action indication 2048_1 may comprise a higher level controltag ‘turn right’; the action indication 20482 may comprise a higherlevel control tag ‘turn left’. Responsive to receipt of sensory input2056, 2026 and/or teaching input 2024, 2054 the predictor 2022 may learnto associate, for example, ‘turn right’ action tag with a series ofmotor instructions (e.g., left wheel rotate forward right, right wheelrotate backwards) with one (or more) features (e.g., object type andlocation) that may be present within the sensory input. Such associations may be referred to as a composite task (e.g., comprising tag and amotor output).

Upon learning these composite tasks, the predictor 2022 may be providedwith a higher level action indication (e.g., 20483). The term ‘higherlevel’ may be used to describe an action (e.g., ‘approach’/‘avoid’) thatmay comprise one or more lower level actions (e.g., 20481, 20482, ‘turnright’/‘turn left’). In some implementations, the higher level actionindication (e.g., 20483) may be combined (by, e.g., the combiner 20303in FIG. 20B) with a predicted higher level action indication (not shownin FIG. 20B). The combined higher level action indication may beprovided to the predictor as a teaching signal and/or sensory input (notshown in FIG. 20B. One or more levels of action indications may form ahierarchy of actions, also referred to as primitives or sub-tasks.

FIG. 21 illustrates one example of a hierarchy of actions for use with,for example, controller of FIG. 20B. An action indication 2100 maycorrespond to a higher level composite action, e.g., ‘approach’,‘avoid’, ‘fetch’, and/or other. The composite action indication 2100 maybe configured to trigger execution of or more actions 2110, 2112, 2114(also referred to as sub-tasks). The sub-tasks 2110, 2112, 2114 maycorrespond to lower level (in the hierarchy of FIG. 21) actions, such as‘turn right’, ‘turn left’, ‘go forward’, respectively.

The sub-tasks (e.g., 2110, 2112, 2114 in FIG. 21) may be associated withone (or more) control output instructions, e.g., signal 2052 describedwith respect to FIG. 20B, supra. individual second level sub-tasks(e.g., 2110, 2112, 2114 in FIG. 21) may be configured to invoke one ormore lower (e.g., third in FIG. 21) level sub-tasks. 2120, 2122 maycorrespond to instructions configured to activate right/left motors ofthe robotic platform. In some implementations, subtasks that may beinvoked by one or more higher level tasks and that may be configured togenerate motor control instructions may be referred to as themotor-primitives (e.g., 2120, 2122 in FIG. 21).

Subtasks of a given level (e.g., 2100, 2108 and/or 2110, 2112, 2114 inFIG. 21) may comprise one or more activation parameters associated withlower level subtasks (e.g., 2110, 2112, 2114, and/or 2120, 2122respectively in FIG. 21). The parameters (e.g., 2102, 2104, 2106) maycomprise one or more of, execution order, weight, turn angle, motionduration, rate of change, torque setting, drive current, shutter speed,aperture setting, and/or other parameters consistent with the planthardware and/or software configuration.

As illustrated in FIG. 21, the task 2100 (e.g., approach target) maycomprise a 30° right turn followed by a 9 second forward motion. Theparameters 2102, 2104, 2106 may be configured as follows:

-   -   0=1, w=30;    -   0=0; and    -   0=2, w=9; respectively.

The task 2108 may correspond to avoid target and may invoke right/leftturn and/or backwards motion tasks 2110, 2112, 2116, respectively.

Individual tasks of the second level (e.g., 2110, 2112, 2114, 2116 inFIG. 21) may cause execution of one or more third level tasks (2120,2122). The parameters 2130, 2132, 2134, 2136, 2138, 2140 may beconfigured as follows:

-   -   to execute right turn: rotate forward left motor with torque of        0.5; (w=0.5), rotate right motor backwards with torque of 0.5;        (w=−0.5);    -   to execute left turn: rotate right motor backwards with torque        of 0.5; (w=−0.5), rotate forward right motor with torque of 0.5;        (w=0.5);    -   to move forward: rotate forward left motor with torque of 0.5;        (w=0.5), rotate forward right motor with torque of 0.5; (w=0.5);        and    -   to move backwards: rotate left motor backwards with torque of        0.5; (w=−0.5), rotate right motor backwards with torque of 0.5;        (w=−0.5).

The hierarchy illustrated in FIG. 21, may comprise another level (e.g.,2130) that may be configured to implement pursue functionality. In oneor more implementations, the pursue functionality mat trigger targetapproach task 2100 and/or obstacle avoidance task 2108.

Control action separation between the predictor 2002, 2022 (configuredto produce the plant control output 2014, 2052) and the controller 2042(configured to provide the action indication 2048) described above, mayenable the controller (e.g., 2042 in FIG. 20B) to execute multiplecontrol actions (e.g., follow a target while avoiding obstacles)contemporaneously with one another.

Control action separation between the predictor 2002, 2022 (configuredto produce the plant control output 2014, 2052) and the controller 2042(configured to provide the action indication 2048) described above, mayenable the controller (e.g., 2042 in FIG. 20B) to execute multiplecontrol actions (e.g., follow a target while avoiding obstacles)contemporaneously with one another.

The controller 2042 may be operable in accordance with a reinforcementlearning (RL) process. In some implementations, the RL process maycomprise a focused exploration methodology, described for example, inco-owned U.S. patent application Ser. No. 13/489,280 entitled “APPARATUSAND METHODS FOR REINFORCEMENT LEARNING IN ARTIFICIAL NEURAL NETWORKS”,filed Jun. 5, 2012, incorporated supra.

Operation of the exemplary RL process comprising focused explorationreinforcement learning methodology may be configured as follows:

$\begin{matrix}{\frac{d\;{\theta_{ij}\left( {t,a} \right)}}{dt} \propto {{R\left( {t,a} \right)}\Sigma_{k}{\eta_{k}\left( {t,a} \right)}{e_{k}\left( {t,a} \right)}}} & \left( {{Eqn}.\mspace{14mu} 19} \right)\end{matrix}$where:

-   -   e,(t) is an adapted parameter (e.g., efficacy) of a synaptic        connection between the pre-synaptic neuron i and the        post-synaptic neuron j;    -   ri(t, a) is a parameter referred to as the learning rate that        scales the 0-changes enforced by learning, II can be a constant        parameter or it can be a function of some other system        parameters;    -   R(t) is a function describing the reward signal;    -   ,(t) is eligibility trace, configured to characterize        correlation between pre-synaptic and post-synaptic activity; and    -   a is a set of parameters that R, _(17k) and e_(k) may dependent        upon.

Eqn. 19 generally describes that synaptic parameter 0₁₁(t)characterizing an interaction of a neuron i and a neuron j, may beadjusted based on a linear combination of individual adjustmentscharacterized by individual learning rates 17 k. Learning combination ofEqn. 19 may be further gated by the reinforcement signal R(t). In someimplementations, the reinforcement signal may be used as a logical(and/or an algebraic switch) for controlling learning.

The predictor 2022 may be operable in accordance with a supervisedlearning (SL) process. In some implementations, the supervised learningprocess may be configured to cause output (e.g., neuron output 144 inFIG. 1B) that is consistent with the teaching signal (e.g., 134 in FIG.1B). Output consistency may be determined based on one or moresimilarity measures, such as correlation, in one or moreimplementations.

Reinforcement learning process of the controller may rely on one or moreexploration techniques. In some implementations, such exploration maycause control output corresponding one or more local minima of thecontroller dynamic state. Accordingly, small changes in the controllerinput (e.g., sensory input 2026 in FIG. 20B) may cause substantialchanges in the controller output responsive to a convergence of thecontroller state to another local minimum. Exploration of reinforcementlearning may require coverage of a full state space associated with thecontroller learning process (e.g., full range of heading, tilt,elevation for a drone searching for a landing strip). State explorationby reinforcement learning may be time consuming and/or may require moresubstantial computational and/or memory resources when compared tosupervised learning (for the same spatial and temporal resolution).Training signal used by supervised learning may limit exploration bypointing to a region within the state space where the target solutionmay reside (e.g., a laser pointer used for illuminating a potentialtarget). In some implementations, the supervised learning may be faster(e.g., converge to target solution with a target precision in shorteramount of time) compared to reinforcement learning. The use of targetsignal during training may enable the SL process to produce a morerobust (less varying) control output for a given set of sensory input,compared to the RL control output. For a given size/capability of asoftware/hardware controller platform, reinforcement learning mayperform fewer tasks (a single task in some implementations) compared tosupervised learning that may enable the controller platform to executeseveral (e.g., 2-10 in some implementations).

In one or more implementations, reinforcement learning signal may beprovided by human operator.

FIG. 13A presents timing diagrams illustrating operation of a combinerapparatus (e.g., 310, 342) of an adaptive controller apparatus (e.g.,200, 220 of FIGS. 2A-2B) configured to generate plant control output.Traces 1300, 1310, 1320 depict activity of controller input into thecombiner (e.g., 308 in FIG. 3A), predicted signal (e.g., 318 in FIG.3A), and the combined output (e.g., 320), respectively. The output ofthe trace 1320 may be obtained in accordance with a union transferfunction, expressed as:u′=h(u,u ^(P))=u∩u ^(P).  (Eqn. 20)

At time t0, the controller input may comprise a control signal V 1302configured to cause the plant to perform a given action (e.g., execute aturn, change speed, and/or other action). In some implementations, suchas illustrated in FIG. 13A, the signal 1302 may comprise spiking signalcomprised of one or more spikes 1308.

The predictor output at time t0, may be absent (e.g., u^(P)=0), asillustrated by absence of spikes on trace 1310 proximate arrow 1312corresponding to time t0. In one or more implementations (not shown),the absence of activity on one or more traces 1300, 1310, 1320 may becharacterized by a base spike rate.

In accordance with the transfer function of Eqn. 20 the combined outputat time t0, may comprise the control input V, as shown by the spikegroup 1322 in FIG. 13A. The combined output (e.g., the spike train 1322)may cause changes in the plant (e.g., 210 in FIG. 2A) state (e.g., wheelangle and/or rotation speed) in accordance with the control signal V(e.g., 40° right turn).

At time t1>t0: the controller input may comprise the control signal V(shown by the spike group 1304 FIG. 13A; the predictor output maycomprise one or more spikes 1314; and the combined output may becomprise a combination of the controller input and predictor output,shown by the spike group 1324 in FIG. 13A. The combined output (e.g.,the spike train 1324) may cause changes in the plant in excess (e.g.,the wheel turn angle greater than 40°) of the state associated with thecontrol signal V.

At time t2>t1: the controller (e.g., 212 in FIG. 2A) may determine thatthe plant state is outside target state (e.g., wheel turn angle greaterthan 40°). The controller may withdraw the control signal as illustratedby absence of spikes proximate arrow 1306 on the trace 1300 in FIG. 13A.The predictor, based on the prior input and/or prior predictor state mayproduce output 1316 at time t2 that may correspond to the initialcombiner output V at time t0 (e.g., the spike train 1322).

FIG. 13B presents timing diagrams illustrating operation of a combinerapparatus (e.g., 302 of FIG. 3A) of an adaptive controller apparatus(e.g., 200, 220 of FIGS. 2A-2B) configured to combine multichannelinputs and generate a single-channel plant control output. Traces 1330,1340, 1350 depict activity of controller input into the combiner (e.g.,308 in FIG. 3A), predicted signal (e.g., 318 in FIG. 3A), and thecombined output (e.g., 320), respectively. The output of the trace 1350may be obtained operable in accordance with an ‘AND’ transfer function,expressed as:û=h(u,u ^(P))=u&u ^(P).  (Eqn. 21)

In some implementations, combiner operation of Eqn. 21 may be expressedas follows: responsive to a single spike on an input channel (e.g., uand/or U), the output may comprise a spike that may be generatedcontemporaneously or with a delay; responsive to multiple spikes on aninput channel (e.g., u and/or U), the output may comprise two or morespikes that may be generated contemporaneously or with a delay relativeinput spikes.

Returning now to FIG. 13B: at time t3, the controller input may comprisecontrol signal V1 1332 configured to cause the plant to perform a givenaction (e.g., execute a turn, change speed, and/or other action). Insome implementations, such as illustrated in FIG. 13B, the controlsignal 1332 may comprise one or more spikes communicated via two or moreconnections 1330.

The predictor output at time t3, may be absent (e.g., u^(P)=0), asillustrated by absence of spikes on trace 1340 proximate arrow 1342corresponding to time t3. In one or more implementations (not shown),the absence of activity on one or more traces 1300, 1310, 1320 may becharacterized by a base spike rate (e.g., 2 Hz).

In accordance with the transfer function of Eqn. 21, the combined output1350 at time t3, may comprise the control input V1, as shown by thespike group 1352 in FIG. 13B. The combined output (e.g., the spike train1352) may cause changes in the plant (e.g., 210 in FIG. 2A) state (e.g.,wheel angle and/or rotation speed) in accordance with the control signalinput V1 (e.g., 45° right turn).

At time t4>t3: the controller input may comprise the control signal V1(shown by the spike group 1334 FIG. 13B; the predictor output maycomprise one or more spikes 1344; and the combined output may becomprise a combination of the controller input and predictor output,shown by the spike group 1354 in FIG. 13B. The combined output (e.g.,the spike train 1354) may cause changes in the plant in excess (e.g.,the turn angle greater than 45°) of the control input associated withthe control signal V1.

At time t5>t4: the controller (e.g., 212 in FIG. 2A) may determine thatthe plant state is outside target state (e.g., turn angle greater than45°). The controller may withdraw the control signal as illustrated byabsence of spikes proximate arrow 1336 on the trace 1330 in FIG. 13B.The predictor, based on the prior input and/or prior predictor state mayproduce output 1346 at time t5 that may predict the initial combineroutput V1 at time t3 (e.g., the spike train 1352).

In some implementations, the predictor output (e.g., shown by the traces1340 in FIG. 13B) may comprise fewer connections compared to number ofconnections associated with the controller input (e.g., 1330 in FIG.13B). A variety of data compression and/or multiple access techniquesmay be utilized in order to transmit predicted data (e.g., spikes 1340in FIG. 13B). In one or more implementations, multiple access techniquesmay include, time division, frequency division, code division multipleaccess. In one or more implementations, predictor output (e.g., 1346 inFIG. 13B) and/or combiner output (e.g., 1356 in FIG. 13B) may be encodedusing sparse coding wherein individual information packets may beencoded by the strong activation of a relatively small set of neurons.Individual items may be encoded using a different subset of availableneurons.

As shown and described with respect to FIGS. 13A-13B, learning by theadaptive controller apparatus (e.g., 200, 220, 300, 330 of FIGS. 2A-3B,respectively) may enable transfer of information (‘knowledge’) from thecontroller output (e.g., 208 in FIG. 2A) to the predictor output (e.g.,218 in FIG. 2A). As used herein the term ‘knowledge’ may refers tochanges to predictor state needed to reproduce, in its predictions(e.g., 238 in FIG. 2B), the signals previously produced by thecontroller output (e.g., 208 in FIG. 2B), but in the absence ofcontinued controller output.

It is noteworthy, that in accordance with the principles of the presentdisclosure, the information transfer (such as described with respect toFIGS. 13A-13B) may occur not instantaneously but gradually on timescales that are in excess of the plant/predictor update intervals.Initially (e.g., at time t0 in FIG. 13A), the controller is capable ofcontrolling the plant in accordance with a target trajectory (e.g.,execute 40° turn). Subsequently (e.g., at time t2 in FIG. 13A), thepredictor may be capable of controlling the plant in accordance with thetarget trajectory. There may exist an intermediate trial state betweent0 and t2 (e.g., t1 in FIG. 13A) wherein: (i) both the predictor and thecontroller are attempting to operate the plant in accordance with thetarget trajectory (e.g., the controller generates the output 1304 andthe predictor generates the output 1314); and (ii) the combined output(e.g., 1324) is inadequate (either too large or too small) to achievethe target trajectory.

In one or more implementations, the predictor may be configured togenerate the predicted signal u^(P) such that it closely reproduces theinitial the control signal u. This is shown in Table 1, where predictedsignal at trial 10 matches the initial control signal at trial 1.

In one or more implementations, such as illustrated in FIG. 16, thepredictor may be configured to predict cumulative (integrated) outcomeof the control action (e.g., the combined output 240 in FIG. 2B).

FIG. 16 presents realizations wherein the predicted signal ischaracterized by temporal distribution having a non-zero time durationassociated therewith. The trace 1600 depicts the target control signalu^(d) (e.g., the output 240 of FIG. 2B). The traces 1610, 1620, 1630depicts the predicted output u^(P) (e.g., the output 238 of FIG. 2B), inaccordance with one or more implementations. Based on the target signalbeing characterized by a temporal distribution (e.g., 5 steps of 9° asshown in Table 2), the predictor may be configured to match theindividual steps (e.g., the shape) of the target output. This isillustrated by the predictor output shape 1612 matching the targetsignal shape 1602 in FIG. 16.

It may be desirable to generate predicted signal configured such thatthe cumulative control effect of the predicted signal matches thecumulative control effect of the target output. In some implementations,such configuration may be expressed as follows: an integral of thepredicted signal over a time span T1 may be configured to match anintegral of the target output u^(d) over a time span T2:∫_(t1) ^(t1+t1) u ^(P)(s)ds=∫ _(t2) ^(t2+T2) u ^(d)(s)ds  (Eqn. 22)

In some implementations, the time intervals Ti, T2 and/or time instancest1, 2 in Eqn. 22 may be configured equal to one another, T1=t2, and/ort1=t2. In one or more implementations, the onset of the predicted signalmay precede the onset of the target signal t1<t2, as illustrated by thecurves 1602, 1612 in FIG. 16.

In some implementations, the predicted signal may be characterized by atemporal distribution that may differ from the target signal temporaldistribution while conforming to the condition of Eqn. 22. Varioustemporal distribution may be utilized with the predictor signal, suchas, for example uniform (curve 1614 in FIG. 16), power law decay (curve1622 in FIG. 16), linear, (curve 1624 in FIG. 16) and/or otherrealizations.

In some implementations comprising processing of visual sensory inputframes refreshed at periodic intervals (e.g., 40 ms), also referred toas the perceptual time scale, predictor generate predicted output thatmay be configured to match the target output when integrated over theperceptual time scale (e.g., the frame duration 1606 in FIG. 16).

FIG. 4 illustrates structure of an adaptive predictor apparatus of, forexample, adaptive controller 200 shown and described with respect toFIG. 2A, above, according to one or more implementations. The adaptivepredictor 402 of FIG. 4 may comprise a spiking neuron network.Individual neurons (e.g., 412, 414, 416, 420) of the predictor 402network may be configured in accordance with any applicablemethodologies (e.g., stochastic and/or deterministic) described, supra.

The network of the predictor 402 may comprise an input neuron layer 410comprised, for example, of the neurons 412, 414, 416 in FIG. 4. Theneurons of the input layer 410 may be configured to receive sensoryinput 406. In some implementations, the sensory input 406 may comprisethe sensory input 106 and/or 206, described with respect to FIGS. 1A, 2Babove. The sensory input 406 may comprise plant feedback (e.g., 2162 inFIG. 2A).

In some object classification applications, individual neurons of theinput layer 410 may be configured to respond to presence and/or absenceof one or more features (e.g., edges, color, texture, sound frequency,and/or other aspects) that may be present within the sensory input 406.In one or more implementations of object recognition and/orclassification may be implemented using an approach comprisingconditionally independent subsets as described in co-owned U.S. patentapplication Ser. No. 13/756,372 filed Jan. 31, 2013, and entitled“SPIKING NEURON CLASSIFIER APPARATUS AND METHODS” and/or co-owned U.S.patent application Ser. No. 13/756,382 filed Jan. 31, 2013, and entitled“REDUCED LATENCY SPIKING NEURON CLASSIFIER APPARATUS AND METHODS”, eachof the foregoing being incorporated, supra.

The sensory input 406 may comprise analog and or spiking input signals.In some implementations, analog inputs may be converted into spikesusing, for example, kernel expansion techniques described in co pendingU.S. patent application Ser. No. 13/623,842 filed Sep. 20, 2012, andentitled “SPIKING NEURON NETWORK ADAPTIVE CONTROL APPARATUS ANDMETHODS”, the foregoing being incorporated, supra. In one or moreimplementations, analog and/or spiking inputs may be processed by mixedsignal spiking neurons, such as U.S. patent application Ser. No.13/313,826 entitled “APPARATUS AND METHODS FOR IMPLEMENTING LEARNING FORANALOG AND SPIKING SIGNALS IN ARTIFICIAL NEURAL NETWORKS”, filed Dec. 7,2011, and/or co-pending U.S. patent application Ser. No. 13/761,090entitled “APPARATUS AND METHODS FOR IMPLEMENTING LEARNING FOR ANALOG ANDSPIKING SIGNALS IN ARTIFICIAL NEURAL NETWORKS”, filed Feb. 6, 2013, eachof the foregoing being incorporated supra.

Output of the input layer 410 neurons may be communicated to aggregationneuron 420 via one or more connections (e.g., 408 in FIG. 4). Theaggregation neuron may be configured to generate predicted output (e.g.,the output 218, 238 in FIGS. 2A-2B). In some implementations, theoperation of the aggregation neuron 420 may be based on training inputsignal. In some implementations, the training input 404 in FIG. 4 maycomprise output 204 of the combiner 234 of FIG. 2B, described above.

In one or more classification and/or regression implementations, thetraining input 404 in FIG. 4 may comprise reference classification input(e.g., an object label) as described in co-owned U.S. patent applicationSer. No. 13/756,372 filed Jan. 31, 2013, and entitled “SPIKING NEURONCLASSIFIER APPARATUS AND METHODS” and/or co-owned U.S. patentapplication Ser. No. 13/756,382 filed Jan. 31, 2013, and entitled“REDUCED LATENCY SPIKING NEURON CLASSIFIER APPARATUS AND METHODS”, eachof the foregoing being incorporated, supra.

FIG. 5 illustrates an exemplary implementation of a neuron networkuseful for implementing adaptive predictor apparatus of a robotic deviceof, for example, FIG. 7, configured for obstacle approach and oravoidance training according to one or more implementations.

The adaptive predictor of FIG. 5 may comprise an artificial neuronnetwork 500 comprising network portions 510, 530, 540. In someimplementations, the network 500 may comprise a network of spikingneuron network operable in accordance with any applicable methodologies(e.g., stochastic and/or deterministic described with respect to FIG.1B, supra).

The network 500 may receive sensory input 506. In some implementations,the sensory input 406 may comprise the sensory input 106 and/or 206,described with respect to FIGS. 1A, 2B above. The sensory input 506 maycomprise plant feedback (e.g., 2162 in FIG. 2A). The sensory input 506may comprise analog and or spiking input signals. In someimplementations, analog inputs may be converted into spikes using anyapplicable methodologies, e.g. those described with respect to FIG. 4supra.

Individual network portions 530, 540 may be configured to implementindividual adaptive predictor realizations. In some implementations, thenetwork portions 530, 540 may implement an object approach predictor andan obstacle avoidance predictor, respectively. The network portion 510may implement a task predictor (e.g., fetch). In some implementations,such as illustrated in FIG. 5, predictors implemented by the networkportions 530, 540, 510 may be configured to form a hierarchy ofpredictors. For example, predictors 530, 540 may form a lower predictorlevel configured to produce control (e.g., motor) primitives, such as538, 548 in FIG. 5. The outputs 538, 548 may also be referred to as thepre-action and/or pre-motor to pre-action output. The output 538 maycomprise predicted obstacle avoidance instructions. The output 548 maycomprise predicted object approach instructions.

The individual network portions 530, 540 may comprise one or more inputlayer neurons and one or more output layer neurons as described indetail with respect to FIG. 4, supra. In some implementations, theoperation of the network portions 530, 540 may be based on traininginput signal, (not shown).

The network portion 510 of FIG. 5 may comprise an input layer comprised,for example, of the neurons 512, 514, 516, 518. The input layer neurons512, 514, 516 may be configured to receive sensory input 506 and thepredicted obstacle avoidance and/or target approach instructions 538,548, respectively.

In some implementations of a fetch task (comprising for example targetapproach and/or obstacle avoidance), the lower level predictors maypredict execution of basic actions (so called, motor primitives), e.g.,rotate left with v=0.5 rad/s for t=10 s.

Predictors of a higher level within the hierarchy, may be trained tospecify what motor primitive to run and with what parameters (e.g., v,t).

At a higher level of hierarchy, the predictor may be configured to plana trajectory and/or predict an optimal trajectory for the robot movementfor the given context.

At yet another higher level of the hierarchy, a controller may beconfigured to determine a behavior that is to be executed at a giventime, e.g. now to execute the target approach and/or to avoid theobstacle.

In some implementations, a hierarchy actions may be expressed as: —toplevel=behavior selection;

-   -   2nd level=select trajectory;    -   3rd level=activate motor primitives to execute given trajectory;        and    -   4th level=issue motor commands (e.g. PWM signal for motors) to        execute the given motor primitives. Outputs of the input layer        neurons 512, 514, 516, 518 may be communicated to aggregation        neuron 526 via one or more connections (e.g., 522 in FIG. 5).        The aggregation neuron may serve several functions, such as, for        example, selection of the best predictions (out of those coming        to the aggregation neuron through the inputs (e.g., 408 in FIG.        4); combining one or more predictions that the neuron may        receive (via, e.g., inputs 408) in accordance with a given        function (e.g., a linear summation, non-linear (weighted)        summation, and/or using other methods). The aggregation neuron        526 of the predictor block 510 may be configured to generate        predicted output 528 (e.g., the output 218, 238 in FIGS. 2A-2B).        In some implementations, the operation of the aggregation neuron        526 may be based on training input signal (not shown). In some        implementations, such training input may comprise the signal 204        of the combiner 234 of FIG. 2B, described above.

In one or more implementations, the adaptive predictor configuration 500illustrated in FIG. 5 may be referred to as a hierarchy of predictorscomprising lower level predictors 530, 540 providing inputs to a higherlevel predictor 510. Such configuration may advantageously alleviate thehigher level predictor 510 from performing all of the functionality thatmay be required in order to implement target approach and/or obstacleavoidance functionality.

The hierarchical predictor configuration described herein may beutilized for teaching a robotic device to perform new task (e.g.,behavior B3 comprised of reaching a target (behavior B1) while avoidingobstacles (behavior B2). The hierarchical predictor realization (e.g.,the predictor 500 of FIG. 5) may enable a teacher (e.g., a human and/orcomputerized operator) to divide the composite behavior B3 into two ormore sub-tasks (B1, B2). In one or more implementations, performance ofthe sub-tasks may be characterized by lower processing requirements bythe processing block associated with the respective predictor (e.g.,530, 540); and/or may require less time in order to arrive at a targetlevel of performance during training, compared to an implementationwherein all of the behaviors (B1, B2, B3) are learned concurrently withone another. Predictors of lower hierarchy (e.g., 530, 540) may betrained to perform sub-tasks B1, B2 in a shorter amount of time usingfewer computational and/or memory resources e.g., of an apparatus 1150illustrated in FIG. 11D, compared to time/resource budget that may berequired for training a single predictor (e.g., 400 of FIG. 4) toperform behavior B3.

When training a higher hierarchy predictor (e.g., 510 in FIG. 5) toperform a new task (e.g., B3 acquire a target), the approach describedabove may enable reuse of the previously learnt task/primitives (B 1B2)and configured the predictor 510 to implement learning of additionalaspects that may be associated with the new task B3, such as B3 areaching and/or B3 b grasping).

If another behavior is to be added to the trained behavior list (e.g.,serving a glass of water), previously learned behavior(s) (e.g.,reaching, grasping, and/or others, also referred to as the primitives)may be utilized in order to accelerate learning compared toimplementations of the prior art.

Reuse of previously learned behaviors/primitives may enable reduction inmemory and/or processing capacity (e.g., number of cores, core clockspeed, and/or other parameters), compared to implementations wherein allbehaviors are learned concurrently. These advantages may be leveraged toincrease processing throughput (for a given neuromorphic hardwareresources) and/or perform the same processing with a reduced complexityand/or cost hardware platform, compared to the prior art.

Learning of behaviors and/or primitives may comprise determining aninput/output transformation (e.g., the function F in Eqn. 10, and/or amatrix F of Eqn. 18) by the predictor. In some implementations, learninga behavior may comprise determining a look-up table and/or an array ofweights of a network as described above. Reuse of previously learnedbehaviors/primitives may comprise restoring/copying stored LUTs and/orweights into predictor realization configured for implementing learnedbehavior.

FIG. 7 illustrates operation of a robotic device 710 (e.g., the rover1070 described with respect to FIG. 10B, below) comprising an adaptivepredictor of the disclosure in an adaptive control system configured toimplement obstacle approach and/or avoidance functionality.

Panel 700 in FIG. 7 depicts exemplary trajectories of the robotic device710 during learning of approaching objects 708, 718 while avoidingcolliding with the objects 708, 718. The control policy of the roveradaptive control system (e.g., the system 200 of FIG. 2A) may beconfigured to cause pass within an area denoted by broken line circle712 from individual objects. In some implementations, approaching thetargets may be useful for performing remote inspections (visual) and maybe associated with positive reinforcement; colliding with the targetsmay be associated with negative reinforcement.

Operational policy of the robotic apparatus may be configured abased ona cost function F(q,t). As used herein, the cost function may beconfigured as the cost-to-go. The costto-go may be described as acumulative of immediate costs C, along a trajectory (e.g., thetrajectory 702, 704 in FIG. 7) of the robotic device and/or whenfollowing an action policy, from a start or intermediate point to afinish point, as described in detail in co-owned U.S. patent applicationSer. No. 13/841,980 filed Mar. 15, 2013, entitled “ROBOTIC TRAININGAPPARATUS AND METHODS”, the foregoing being incorporated herein byreference in its entirety.

In some implementations, selecting a policy may comprise providing theadaptive apparatus (e.g., a robot) with an exemplary trajectory overwhich the robot is directed to follow as close as possible and/or pointsto which the robot is directed to reach during its path to the goal.

The trajectory 702 may correspond to first learning trial, wherein therobotic device 710 may have no prior knowledge/encounters with objects.As shown in FIG. 7, the robot 710 following the trajectory 702 collideswith the object 708.

Based on learning experience during traverse of the trajectory 702, theadaptive predictor of the robotic device 710 may encounter sensory inputstates xi and/or controller actions ui associated withapproaching/avoiding objects 708/618.

Subsequently, during traversal of the trajectory 704, the adaptivepredictor of the robotic device 710 may utilize the prior sensoryexperience and improve its prediction performance as illustrating byabsence of collisions associated with the trajectory 704.

During traversal of the trajectory 706, the performance of the adaptivepredictor may improve further thereby producing target performance atlowest cost.

The knowledge gained by the predictor (e.g., 222 in FIG. 2B) duringlearning trajectories 702, 704, 706 may be utilized in subsequentoperations illustrated in panel 720 in FIG. 7. The data transfer isshown diagrammatically by broken curve 716 in FIG. 7. The term knowledgemay describe a set of learned dependencies (and/or associations) betweenthe inputs and the outcomes (actions). In some learn to navigateimplementations, the knowledge may refer to a learned mapping betweenthe sensory and contextual information and the appropriate motorcommands for navigation. In some implementations, the knowledge mayrefer to state-to-action transformation performed by the predictorduring the traverse of the trajectories 702, 704, 706. Thestate-to-action transformation may comprise a link between given stateand the action performed for that state.

The panel 720 depicts exemplary trajectories of the robotic device 710during learning of approaching objects 728, 738 while avoiding collidingwith the objects. The locations and or orientation of objects 728, 738may be configured different compared to objects 708, 718 in panel 700.

Based on prior learning experience, the predictor may be capable ofpredicting control signal (e.g., 238 in FIG. 2B) with accuracy that maybe sufficient for achieving target performance during traverse of thetrajectory 724 shown in panel 720. The target performance may be basedon minimal distance between the robot and the targets, used energy,cumulative or and/or maximum absolute deviation from target trajectory,traverse time, and/or other characteristics.

FIGS. 8A-9, 17-19B illustrate methods of operating adaptive predictorapparatus of the disclosure in accordance with one or moreimplementations. The operations of methods 800, 820, 840, 900, 1700,1800, 1900, 1920 presented below are intended to be illustrative. Insome implementations, methods 800, 820, 840, 900, 1700, 1800, 1900, 1920may be accomplished with one or more additional operations notdescribed, and/or without one or more of the operations discussed.Additionally, the order in which the operations of methods 800, 820,840, 900, 1700, 1800, 1900, 1920 are illustrated in FIGS. 8A-9, 17-19Band described below is not intended to be limiting.

In some implementations, methods 800, 820, 840, 900, 1700, 1800, 1900,1920 may be implemented in one or more processing devices (e.g., adigital processor, an analog processor, a digital circuit designed toprocess information, an analog circuit designed to process information,a state machine, and/or other mechanisms for electronically processinginformation). The one or more processing devices may include one or moredevices executing some or all of the operations of methods 800, 820,840, 900, 1700, 1800, 1900, 1920 in response to instructions storedelectronically on an electronic storage medium. The one or moreprocessing devices may include one or more devices configured throughhardware, firmware, and/or software to be specifically designed forexecution of one or more of the operations of methods 800, 820, 840,900, 1700, 1800, 1900, 1920.

At operation 802 of method 800, illustrated in FIG. 8A sensory contextmay be determined. In some implementations, the context may comprise onor more aspects of sensory input (e.g., 206) and/or plant feedback (216in FIG. 2A). In one or more implementations, the sensory aspects mayinclude an object being detected in the input, a location of the object,a n object characteristic (color/shape), a sequence of movements (e.g.,a turn), a characteristic of an environment (e.g., an apparent motion ofa wall and/or other surroundings turning a turn and/or approach)responsive to the movement. In some implementation, the sensory inputmay be received based on performing one or more training trials (e.g.,as the trials described with respect to Table 1-Table 3 above) of arobotic apparatus.

At operation 804, a predicted output may be generated based on thecontext and a teaching signal. In some implementations, the teachingsignal may comprise an output of a combiner apparatus (e.g., 204 in FIG.2A) that may be configured based on predicted output from a prior trial.The teaching signal may comprise action indications. In one or moreimplementations, the teaching signal may be provided by a controller(e.g., 212 in FIG. 2B and/or 302 in FIG. 3A) and/or a supervisor (e.g.,a human or computerized agent). The output of operation 804 maycorrespond to predictor output of a training trial (e.g., the trial 1410of FIG. 14).

At operation 806 the predicted output may be provided for generating theteaching signal configured to be used by the predictor at, for example,a subsequent trial (e.g., the trial 1412 of FIG. 14).

FIG. 8B illustrates a method of training a robotic device comprising anadaptive predictor, in accordance with one or more implementations. Therobotic apparatus may be configured to perform one or more tasks (e.g.,approach a target).

At operation 822 of method 820, sensory context may be determined. Insome implementations, the context may comprise on or more aspects ofsensory input, as described above with respect to operation 802 of FIG.8A.

At operation 824, training of the predictor apparatus may commence inorder to generate predicted control output based, at least in part, onthe context.

At operation 826, a predicted control signal u1′ may be generated basedon the context and a control signal. The control signal may correspondto an output (e.g., 208) of controller. In some implementations, thepredictor may determine the control signal based on one or more ofsensory input (e.g., 206), plant feedback (216), and/or prior predictorstate Q associated with the context that may have occurred previously.The predicted output may comprise a control command (e.g., turn by 9°).In some implementations e.g., as illustrated and described with respectto FIG. 2B, the predicted output may be generated based a teachingsignal. The teaching signal (e.g., 204 in FIG. 2B) may comprise acombination of the predicted control output and the control signal(e.g., 240 in FIG. 2B). Operation 826 may be executes as a part of atraining trial (e.g., the trial 1410 of FIG. 14).

At operation 828, plant of the robotic apparatus may be operated basedon a combination of the predicted control output u2^(P) and the controlsignal.

At operation 830, at another trial Ti>T1 predicted control signal ui^(P)may be determined based on the control signal and prior predictedcontrol output u1.

At operation 832, at the trial Tj>T1 plant of the robotic apparatus maybe operated based on a combination of the predicted control outputui^(P) and the control signal.

At operation 834 a determination may be made as to whether additionaltrials may be performed. If another trial is to be performed, the methodmay proceed to step 828.

FIG. 8C illustrates a method of operating an adaptive predictor of arobotic device based on sensory context, in accordance with one or moreimplementations.

At operation 842 of method 840, sensory input may be received. The inputreceived at operation 842 may be characterized by a context associatedtherewith. In some implementations, the sensory input and/or context maycomprise on or more aspects, such as described above with respect tooperation 802 of FIG. 8A.

At operation 844, a determination may be made as to whether the contextassociated with the input received at operation 842 a corresponds to newcontext or previously observed context. As used herein, the term ‘newcontext’ may be used to describe context that was not present in thesensory input, immediately prior to operation 842. In someimplementations, such as illustrated and described with respect to FIG.14, and/or Table 2-Table 3, the context may correspond to a presence ofan obstacle or an object in the sensory input. Based on the same objectand/or obstacle being present in the sensory input during two or moretrials and training information from one (or more) prior trials (e.g.,prior object/obstacle type shape, color, position, and/or othercharacteristic, and/or prior predictor state (e.g., neuron networkconfiguration, neuron states (excitability), connectivity (e.g.,efficacy of connections) be available during a subsequent trial, suchcontext may be considered as previously observed (e.g., not new).

Responsive to a determination at operation 844 that the present sensorycontext has not been previously observed, the method 840 may proceed tooperation 846 wherein predictor state Q may be determined based on theinputs into the predictor (e.g., inputs 206, 216 in FIG. 2A). In someimplementations, such as shown and described with respect to FIG. 9,below, the predictor state may be based on a training signal (e.g., 204in FIG. 2B). In one or more implementations, the predictor statedetermination of operation 846 may correspond to training configurationshown and described with respect to Table 2, supra.

Responsive to a determination at operation 844 that the present sensorycontext has been previously observed, the method 840 may proceed tooperation 848 wherein an updated predictor state Q may be determinedbased on the inputs into the predictor (e.g., inputs 206, 216 in FIG.2A), and a previous predictor state Q(x1) associated with the contextx1. In some implementations, such as shown and described with respect toFIG. 9, below, the predictor state Q update may be based on a trainingsignal (e.g., 204 in FIG. 2B). In one or more implementations, thepredictor state determination of operation 848 may correspond totraining configuration shown and described with respect to Table 2,supra.

At operation 850, updated predictor state Q′(x1) may be stored. In someimplementations, the predictor state may be stored in locally (e.g., inmemory 1134 in FIG. 11B and or micro block L1 1152 of FIG. 11D). In oneor more implementations, the predictor state may be stored in a commonlyavailable storage (e.g., 1108 and/or 1106 of FIG. 11A).

At operation 852, predicted control output (e.g., 218, 238 in FIGS.2A-2B) may be generated based on the updated predictor state. In someimplementations, the predicted output of operation 852 may correspond tooutput u^(P) shown and described with respect to Table 1-Table 3.

FIG. 9 illustrates a method of operating an adaptive predictorcomprising a training input, in accordance with one or moreimplementations.

At operation 902 of method 900, sensory input may be received. The inputreceived at operation 902 may be characterized by a context associatedtherewith. In some implementations, the sensory input and/or context maycomprise on or more aspects, such as described above with respect tooperation 802 of FIG. 8A.

At operation 904, a determination may be made as to whether the teachingsignal is available to the predictor. In some implementations, such asshown and described with respect to FIGS. 2B-3E, supra, the trainingsignal may comprise target predictor output that may be determined basedon a combination of the controller output (e.g., 208 in FIG. 2B) andpredictor output from a prior trial (e.g., as described by Eqn. 9).

Responsive to a determination at operation 904 that the teaching signalis available, the method 900 may proceed to operation 906 whereinpredictor state Q may be determined based on the inputs into thepredictor (e.g., inputs 206, 216 in FIG. 2A), and the teaching signal(e.g., 304 in FIG. 3B). In some implementations, the predictor state maycomprise one (or more) of: neuron network configuration (e.g., numberand/or type of neurons and/or neuron connectivity), neuron states(excitability), and connectivity (e.g., efficacy of connections). Insome implementations, the network configuration may comprise neuronstate parameter characterizing neuron intrinsic plasticity. In one ormore implementations, the predictor state may comprise one or more LUT(e.g., as illustrated by Table 4-Table 5), a database comprising one ormore tables; and/or a hash-table. In some implementations, the predictorstate may comprise a bit-file configured to characterize machine codeand/or memory content of the predictor processing apparatus (e.g., aprocessing core, a CPU, a DSP, and/or FPGA).

Responsive to a determination at operation 904 that the teaching signalis not available, the method 900 may proceed to operation 908 whereinpredictor state Q may be determined based on the inputs into thepredictor (e.g., inputs 206, 216 in FIG. 2A.

At operation 910, updated predictor state Q′(x1) may be stored. In someimplementations, the predictor state may be stored in locally (e.g., inmemory 1134 in FIG. 11B and or micro block L1 1152 of FIG. 11D). In oneor more implementations, the predictor state may be stored in a commonlyavailable storage (e.g., 1108 and/or 1106 of FIG. 11A).

At operation 902, predicted control output (e.g., 218, 238 in FIGS.2A-2B) may be generated based on the updated predictor state. In someimplementations, the predicted output of operation 902 may correspond tooutput u^(P) shown and described with respect to Table 1-Table 3.

FIG. 17 illustrates a method of developing a hierarchy of control tasksby a controller comprising an adaptive predictor, in accordance with oneor more implementations.

At operation 1702, a given task may be partitioned into two (or more)sub-tasks. In some implementations, such as a task of training of arobotic manipulator to grasp a particular object (e.g., a cup), thesubtasks may correspond to identifying the cup (among other objects);approaching the cup, avoiding other objects (e.g., glasses, bottles),and/or grasping the cup. A subtask predictor may comprise actionindication predictor.

At operation 1704, an predictor for an individual sub-task may betrained in accordance with sensory input x. In one or moreimplementations, individual sub-task predictor may comprise one or morepredictor configurations described, for example, with respect to FIGS.2A-3E, 4, described above.

At operation 1706, trained predictor configuration may be stored. In oneor more implementations, the trained predictor configuration maycomprise one (or more) of neuron network configuration (e.g., numberand/or type of neurons and/or connectivity), neuron states(excitability), connectivity (e.g., efficacy of connections).

At operation 1708, sub-task predictor may be operated in accordance withthe sub-task predictor configuration and the sensory input. In someimplementations of a predictor corresponding to a composite task (e.g.,2100, 2110, 2112 in FIG. 21), predictor operation may comprisedetermining which lower level (within the hierarchy) predictors are tobe activated, and/or plant control output is to be generated. In someimplementations of a predictor corresponding to the lowest level task(e.g., 2120, 2122, in FIG. 21), predictor operation may comprisegeneration of control output.

At operation 1710, a determination may be made as to whether additionalsubtask predictor may need to be trained. In some implementations, thepredictor may be configured to perform the determination. In one or moreimplementations, a controller (e.g., 212 in FIG. 2B) and/or a teacher(e.g., an external human and/or computerized agent) may be configured toperform the determination.

Responsive to a determination that no additional subtasks remain, themethod may proceed to step 1712 where output u^(P) for the taskpredictor may be generated in accordance with the sensory input x andoutcomes of the sub-task predictor operation at operation 1708.

FIG. 18 illustrates a method of operating an adaptive control systemcomprising a signal combiner and an adaptive predictor apparatus of,e.g., FIG. 3A, in accordance with one or more implementations.

At operation 1802 of method 1800, the signal combiner may receive anM-channel control signal associated with a sensory context. In someimplementations, the sensory input and/or context may comprise on ormore aspects, such as described above with respect to operation 802 ofFIG. 8A. In one or more implementations of spiking neuron networkcontrol system, the M-channel control signal may correspond to a spikeinput received by a spiking neuron (e.g., 140 of FIG. 1B) of thecombiner via M connections (e.g., 124 in FIG. 1B). The spike input maycomprise one to M spike trains (e.g., 1332 in FIG. 13B).

At operation 1804, the combiner may receive N-channel predictor outputassociated with the sensory context. In one or more implementations ofspiking neuron network control system, the N-channel predictor outputmay correspond to a spike output (e.g., 1346 in FIG. 13B) communicatedby the predictor via one to N connections

At operation 1806, the N-channel predicted output and the M-channelcontrol signal may be combined to produce combined control output. Inone or more implementations of spiking neuron network control system,the combined output may correspond to a spike train e.g., 1356 in FIG.13B) communicated by spiking neuron of the combiner. In someimplementations, the combined output (e.g., shown by the trace 1350 inFIG. 13B) may comprise fewer channels (dimensions) compared to number ofchannels/dimensions M associated with the controller input (e.g., 1330in FIG. 13B), and/or number of channels/dimensions N associated with thepredictor input (e.g., 1330 in FIG. 13B). A variety of data compressionand/or multiple access techniques may be utilized in order to combinethe predicted data stream (e.g., spikes 1344 in FIG. 13B) with thecontroller data stream (e.g., spikes 1334 in FIG. 13B) into output datastream (e.g., spikes 1354 in FIG. 13B). In one or more implementations,multiple access techniques may be employed, such as, time division,frequency division, code division multiple access. In one or moreimplementations, predictor output (e.g., 1346 in FIG. 13B) and/orcombiner output (e.g., 1356 in FIG. 13B) may be encoded using sparsecoding wherein individual information packets may be encoded by thestrong activation of a relatively small set of neurons. Individual itemsmay be encoded using a different subset of available neurons.

At operation 1808, combined output may be provided. In someimplementations, the combined output (e.g., 240 in FIG. 2B) may beemployed for controlling a plant (e.g., 210 in FIG. 2B). In one or moreimplementations, the combined output may be utilized (e.g., vi ateaching signal 204 in FIG. 2B) in generating predicted control signalat a subsequent time.

FIG. 19A illustrates a method of developing an association between anaction indication and sensory context for use in an adaptive predictorapparatus of, e.g., FIG. 20A, in accordance with one or moreimplementations.

At operation 1902 of method 1900, an action indication, configured basedon sensory context x1, may be received by the predictor at time t1. Inone or more implementations, the sensory context x1 may comprise one ormore characteristics of an object (e.g., location of target 1208 in FIG.12); the action indication A may comprise an action tag, e.g., A=‘turn’.The adaptive predictor may be operated in accordance with a learningprocess configured to develop an association between the action tag andthe sensory context. The predictor may be configured to generate plantcontrol output (e.g., 2014 in FIG. 20A) based on the developedassociation and/or receipt of the action indication. The learningprocess may be aided by a teaching signal (e.g., 2004 in FIG. 20A). Insome implementations, the teaching signal may enable learning imitation,e.g., wherein the predictor is configured to mimic the teaching signal(e.g., the correct motor commands for the given context) as described,for example, in U.S. patent application Ser. No. 13/313,826 entitled“APPARATUS AND METHODS FOR IMPLEMENTING LEARNING FOR ANALOG AND SPIKINGSIGNALS IN ARTIFICIAL NEURAL NETWORKS”, filed Dec. 7, 2011, U.S. patentapplication Ser. No. 13/722,769 filed Dec. 20, 2012, and entitled“APPARATUS AND METHODS FOR STATE-DEPENDENT LEARNING IN SPIKING NEURONNETWORKS”, and/or pending U.S. patent application Ser. No. 13/761,090entitled “APPARATUS AND METHODS FOR IMPLEMENTING LEARNING FOR ANALOG ANDSPIKING SIGNALS IN ARTIFICIAL NEURAL NETWORKS”, filed Feb. 6, 2013,incorporated supra. In some implementations, the predictor may beconfigured to learn through reinforcement learning, as described, forexample, in U.S. patent application Ser. No. 13/489,280 entitled“APPARATUS AND METHODS FOR REINFORCEMENT LEARNING IN ARTIFICIAL NEURALNETWORKS”, filed Jun. 5, 2012, incorporated supra. Upon learning theassociation between the tag A and the context x1, the predictor may becharacterized by predictor state Q(A, x1). In some implementations of apredictor comprising a spiking neuron network, the associationinformation may comprise one or more network connectivity, neuron state,and/or connection efficacy (e.g., weights). The developed associationinformation (e.g., predictor state Q(A, x1) may be stored in, forexample, shared memory 1106, 1108 of FIG. 11A, and/or cell memory 1134of FIG. 11B.

In some implementations, during learning, association development by thepredictor may be aided by plant control commands (e.g., 2046 in FIG.20B) issued by the controller (2042 in FIG. 20B). The control command2046 may act as a training input via the pathway 2034 in FIG. 20B forthe predictor learning process. Subsequent to learning, once thepredictor has associated the action indicator with the sensory context,the low-level control output (e.g., 2046) may be withdrawn by thecontroller.

At operation 1904 at a subsequent time instance t2>t1, the sensorycontext x1 may be detected by the predictor in sensory input (e.g., 2006in FIG. 20A). In one or more implementations, the context x1 maycorrespond to a location of a target (e.g., 1208 in FIG. 12).

At operation 1906 at a subsequent time instance t3>t1, a determinationmay be made as to whether the control action indication is present. Inone or more implementations, the action indication A may comprise thecontrol tag A=‘turn’, (e.g., 1204 in FIG. 12).

Responsive to detecting the presence of the action tag at operation1906, the method 1900 may proceed to step 1908 wherein the predictor maygenerate the plant control output capable of causing the plant toperform the action in absence of contemporaneous control input (e.g.,2046 in FIG. 20B) from the controller. In some implementations, theplant control signal generation may be effectuated based on theassociation information (e.g., predictor state Q(A, x1)) developed atoperation 1902 and/or stored in, for example, shared memory 1106, 1108of FIG. 11A, and/or cell memory 1134 of FIG. 11B.

FIG. 19B illustrates a method of operating an adaptive predictorapparatus of, e.g., FIG. 20B, using a hierarchy of previously developedassociations between an action indication and sensory context for, inaccordance with one or more implementations.

At operation 1922 of method 1920, a determination may be made as towhether an action indication is present. In one or more implementations,the action indication may correspond to one or more tasks 2100, 2110,2112, 2114, described with respect to FIG. 21 above.

Responsive to detecting the presence of the action tag at operation1922, the method 1920 may proceed to operation 1924 wherein adetermination may be made as to whether the action comprises a compositeaction and/or a primitive action (e.g., 2120 in FIG. 21).

Responsive to determining operation 1924 that the action comprises acomposite action (e.g., the task 2110 in FIG. 21 configured to triggermotor primitives 2120, 2122), the method 1920 may proceed to step 1926wherein sub-action information may be accessed. Access of the sub-actioninformation may comprise one or more operations such as identifying thesub-action (e.g., activate right motor), providing operationalparameters for executing control output associated with the sub-action(e.g., rotate left motor forward at a torque 0.5).

At operation 1928 of method 1920, predictor configuration associatedwith the sub-action may be accessed. In some implementations, predictorconfiguration may comprise predictor state (e.g., network weights)determined during the association between the action indication andsensory context at a prior time (e.g., as described with respect tomethod 1900 of FIG. 19). In one or more implementations, previouslystored predictor configuration (e.g., a vector of efficacies) may beloaded into a neuron process (e.g., neuron 120 of FIG. 1B, and/or into ablock of neuromorphic computing apparatus (e.g., the block L1 1152, L21154, L3 1156 of FIG. 11D described below).

At operation 1930 of method 1920, a determination may be made as towhether an additional sub action indication is present. In someimplementations, a given (sub) action of a given level (e.g., 2112 inFIG. 21) may invoke multiple lower level sub-actions (e.g., 2120, 2122).In such implementations, multiple instances of neuron 120 and/or blockof neuromorphic computing block may be instantiated with configurationof the respective predictor, e.g., based on a prior association for theaction indication “right turn”, one block of the neuromorphic apparatus1150 may be configured with the predictor state configured to generateright motor instructions (sub-action 1); and another block of theneuromorphic apparatus 1150 may be configured with the predictor stateconfigured to generate left motor instructions (sub-action 2).Responsive to determining at operation 1928 that additional sub-actionsare to be initiated the method may proceed to operation 1926.

Responsive to determining at operation 1928 that no additionalsub-actions are to be initiated, the method may proceed to operation1930 wherein predicted control output may be generated (by one or morepredictor blocks) in accordance with the previously learned sub-actionconfiguration.

At operation 1932, the plant may be operated (e.g., execute a 30° rightturn) in accordance with the predicted motor control output configuredbased on the action indication of operation 1922 (e.g., turn right) andsensory input (e.g., input 2126, object at 30° to the right).

Adaptive predictor methodologies described herein may be utilized in avariety of processing apparatus configured to, for example, implementtarget approach and/or obstacle avoidance by autonomous robotic devicesand/or sensory data processing (e.g., object recognition).

One approach to object recognition and/or obstacle avoidance maycomprise processing of optical flow using a spiking neural networkcomprising for example the self-motion cancellation mechanism, such asdescribed, for example, in U.S. patent application Ser. No. 13/689,717,entitled “APPARATUS AND METHODS FOR OBJECT DETECTION VIA OPTICAL FLOWCANCELLATION”, filed Nov. 30, 2012, the foregoing being incorporatedherein by reference in its entirety, is shown in FIG. 10A. Theillustrated processing apparatus 1000 may comprise an input interfaceconfigured to receive an input sensory signal 1002. In someimplementations, this sensory input may comprise electromagnetic waves(e.g., visible light, IR, UV, and/or other types of electromagneticwaves) entering an imaging sensor array. The imaging sensor array maycomprise one or more of RGCs, a charge coupled device (CCD), anactive-pixel sensor (APS), and/or other sensors. The input signal maycomprise a sequence of images and/or image frames. The sequence ofimages and/or image frame may be received from a CCD camera via areceiver apparatus and/or downloaded from a file. The image may comprisea two-dimensional matrix of RGB values refreshed at a 25 Hz frame rate.It will be appreciated by those skilled in the arts that the above imageparameters are merely exemplary, and many other image representations(e.g., bitmap, CMYK, HSV, grayscale, and/or other representations)and/or frame rates are equally useful with the present invention. Theapparatus 1000 may be embodied in, for example, an autonomous roboticdevice, e.g., the device 1060 of FIG. 10B.

The apparatus 1000 may comprise an encoder 1010 configured to transform(e.g., encode) the input signal 1002 into an encoded signal 1026. Insome implementations, the encoded signal may comprise a plurality ofpulses (also referred to as a group of pulses) configured to representto optical flow due to one or more objects in the vicinity of therobotic device.

The encoder 1010 may receive signal 1004 representing motion of therobotic device. In one or more implementations, the input 1004 maycomprise an output of an inertial sensor block. The inertial sensorblock may comprise one or more acceleration sensors and/or accelerationrate of change (i.e., rate) sensors. In one or more implementations, theinertial sensor block may comprise a 3-axis accelerometer and/or 3-axisgyroscope. It will be appreciated by those skilled in the arts thatvarious other motion sensors may be used to characterized motion of arobotic platform, such as, for example, radial encoders, range sensors,global positioning system (GPS) receivers, RADAR, SONAR, LIDAR, and/orother sensors.

The encoder 1010 may comprise one or more spiking neurons. One or moreof the spiking neurons of the block 1010 may be configured to encodemotion input 1004. One or more of the spiking neurons of the block 1010may be configured to encode input 1002 into optical flow, as describedin U.S. patent application Ser. No. 13/689,717, entitled “APPARATUS ANDMETHODS FOR OBJECT DETECTION VIA OPTICAL FLOW CANCELLATION”, filed Nov.30, 2012, incorporated supra.

The encoded signal 1026 may be communicated from the encoder 1010 viamultiple connections (also referred to as transmission channels,communication channels, or synaptic connections) 1044 to one or moreneuronal nodes (also referred to as the detectors) 1042.

In the implementation of FIG. 10A, individual detectors of the samehierarchical layer may be denoted by a “n” designator, such that e.g.,the designator 1042_1 denotes the first detector of the layer 1042.Although only two detectors (1042_1, 1042_n) are shown in theimplementation of FIG. 10 for clarity, it will be appreciated that theencoder may be coupled to any number of detector nodes that iscompatible with the detection apparatus hardware and softwarelimitations. Furthermore, a single detector node may be coupled to anypractical number of encoders.

In one implementation, individual detectors 1042_1, 1042_n may containlogic (which may be implemented as a software code, hardware logic, or acombination of thereof) configured to recognize a predetermined patternof pulses in the encoded signal 1026 to produce post-synaptic detectionsignals transmitted over communication channels 1048. Such recognitionmay include one or more mechanisms described in U.S. patent applicationSer. No. 12/869,573, filed Aug. 26, 2010 and entitled “SYSTEMS ANDMETHODS FOR INVARIANT PULSE LATENCY CODING”, U.S. patent applicationSer. No. 12/869,583, filed Aug. 26, 2010, entitled “INVARIANT PULSELATENCY CODING SYSTEMS AND METHODS”, U.S. patent application Ser. No.13/117,048, filed May 26, 2011 and entitled “APPARATUS AND METHODS FORPOLYCHRONOUS ENCODING AND MULTIPLEXING IN NEURONAL PROSTHETIC DEVICES”,U.S. patent application Ser. No. 13/152,084, filed Jun. 2, 2011,entitled “APPARATUS AND METHODS FOR PULSE-CODE INVARIANT OBJECTRECOGNITION”, each of the foregoing incorporated herein by reference inits entirety. In FIG. 10A, the designators 1048_1, 1048_n denote outputof the detectors 10421, 1042 n, respectively.

In some implementations, the detection signals may be delivered to anext layer of detectors 1052 (comprising detectors 10521, 1052 m,1052_k) for recognition of complex object features and objects, similarto the exemplary implementation described in commonly owned andco-pending U.S. patent application Ser. No. 13/152,084, filed Jun. 2,2011, entitled “APPARATUS AND METHODS FOR PULSE-CODE INVARIANT OBJECTRECOGNITION”, incorporated supra. In such implementations, individualsubsequent layers of detectors may be configured to receive signals fromthe previous detector layer, and to detect more complex features andobjects (as compared to the features detected by the preceding detectorlayer). For example, a bank of edge detectors may be followed by a bankof bar detectors, followed by a bank of corner detectors and so on,thereby enabling recognition of one or more letters of an alphabet bythe apparatus.

Individual detectors 1042 may output detection (post-synaptic) signalson communication channels 10481, 1048_n (with an appropriate latency)that may propagate with different conduction delays to the detectors1052. The detector cascade of the implementation of FIG. 10 may containany practical number of detector nodes and detector banks determined,inter alia, by the software/hardware resources of the detectionapparatus and complexity of the objects being detected.

The sensory processing apparatus 1000 illustrated in FIG. 10A mayfurther comprise one or more lateral connections 1046, configured toprovide information about activity of neighboring neurons to oneanother.

In some implementations, the apparatus 1000 may comprise feedbackconnections 1006, 1056, configured to communicate context informationfrom detectors within one hierarchy layer to previous layers, asillustrated by the feedback connections 10561, 1056_2 in FIG. 10. Insome implementations, the feedback connection 1006 may be configured toprovide feedback to the encoder 1010 thereby facilitating sensory inputencoding, as described in detail in commonly owned and co-pending U.S.patent application Ser. No. 13/152,084, filed Jun. 2, 2011, entitled“APPARATUS AND METHODS FOR PULSE-CODE INVARIANT OBJECT RECOGNITION”,incorporated supra.

FIG. 10B a mobile robotic apparatus that may be configured with anadaptive controller implementation illustrated in FIG. 10A, supra. Therobotic apparatus 1060 may comprise a camera 1066. The camera 1066 maybe characterized by a field of view 1068. The camera 1066 may provideinformation associated with objects within the field-of-view. In someimplementations, the camera 1066 may provide frames of pixels ofluminance, refreshed at 25 Hz frame rate.

One or more objects (e.g., a stationary object 1074 and a moving object1076) may be present in the camera field of view. The motion of theobjects may result in a displacement of pixels representing the objectswithin successive frames, such as described in U.S. patent applicationSer. No. 13/689,717, entitled “APPARATUS AND METHODS FOR OBJECTDETECTION VIA OPTICAL FLOW CANCELLATION”, filed Nov. 30, 2012,incorporated, supra.

When the robotic apparatus 1060 is in motion, such as shown by arrow1064 in FIG. 10B, the optical flow estimated from the image data maycomprise the self-motion component and the object motion component. Byway of a non-limiting example, the optical flow measured by the rover ofFIG. 10B may comprise one or more of (i) self-motion components of thestationary object 1078 and the boundary (e.g., the component 1072associated with the floor boundary); (ii) component 1080 associated withthe moving objects 116 that comprises a superposition of the opticalflow components due to the object displacement and displacement of therobotic apparatus, and/or other components.

Various exemplary spiking network apparatuses configured to perform oneor more of the methods set forth herein (e.g., adaptive predictorfunctionality) are now described with respect to FIGS. 11A-11D.

One particular implementation of the computerized neuromorphicprocessing system, for use with an adaptive robotic controllerdescribed, supra, is illustrated in FIG. 11A. The computerized system1100 of FIG. 11A may comprise an input device 1110, such as, forexample, an image sensor and/or digital image interface. The inputinterface 1110 may be coupled to the processing block (e.g., a single ormulti-processor block) via the input communication interface 1114. Insome implementations, the interface 1114 may comprise a wirelessinterface (cellular wireless, Wi-Fi, Bluetooth, etc.) that enables datatransfer to the processor 1102 from remote I/O interface 1100, e.g. Onesuch implementation may comprise a central processing apparatus coupledto one or more remote camera devices providing sensory input to theadaptive predictor block (e.g., block 202 in FIG. 2A).

The system 1100 further may comprise a random access memory (RAM) 1108,configured to store neuronal states and connection parameters and tofacilitate synaptic updates. In some implementations, synaptic updatesmay be performed according to the description provided in, for example,in U.S. patent application Ser. No. 13/239,255 filed Sep. 21, 2011,entitled “APPARATUS AND METHODS FOR SYNAPTIC UPDATE IN A PULSE-CODEDNETWORK”, incorporated by reference, supra

In some implementations, the memory 1108 may be coupled to the processor1102 via a direct connection 1116 (e.g., memory bus). The memory 1108may also be coupled to the processor 1102 via a high-speed processor bus1112.

The system 1100 may comprise a nonvolatile storage device 1106. Thenonvolatile storage device 1106 may comprise, inter alia, computerreadable instructions configured to implement various aspects of spikingneuronal network operation. Examples of various aspects of spikingneuronal network operation may include one or more of sensory inputencoding, connection plasticity, operation model of neurons, learningrule evaluation, other operations, and/or other aspects. In one or moreimplementations, the nonvolatile storage 1106 may be used to store stateinformation of the neurons and connections for later use and loadingpreviously stored network configuration. The nonvolatile storage 1106may be used to store state information of the neurons and connectionswhen, for example, saving and/or loading network state snapshot,implementing context switching, saving current network configuration,and/or performing other operations. The current network configurationmay include one or more of connection weights, update rules, neuronalstates, learning rules, and/or other parameters.

In some implementations, the computerized apparatus 1100 may be coupledto one or more of an external processing device, a storage device, aninput device, and/or other devices via an I/O interface 1120. The I/Ointerface 1120 may include one or more of a computer I/O bus (PCI-E),wired (e.g., Ethernet) or wireless (e.g., Wi-Fi) network connection,and/or other I/O interfaces.

In some implementations, the input/output (I/O) interface may comprise aspeech input (e.g., a microphone) and a speech recognition moduleconfigured to receive and recognize user commands.

It will be appreciated by those skilled in the arts that variousprocessing devices may be used with computerized system 1100, includingbut not limited to, a single core/multicore CPU, DSP, FPGA, GPU, ASIC,combinations thereof, and/or other processors. Various user input/outputinterfaces may be similarly applicable to implementations of theinvention including, for example, an LCD/LED monitor, touch-screen inputand display device, speech input device, stylus, light pen, trackball,and/or other devices.

Referring now to FIG. 11B, one implementation of neuromorphiccomputerized system configured to implement classification mechanismusing a spiking network is described in detail. The neuromorphicprocessing system 1130 of FIG. 11B may comprise a plurality ofprocessing blocks (micro-blocks) 1140. Individual micro cores maycomprise a computing logic core 1132 and a memory block 1134. The logiccore 1132 may be configured to implement various aspects of neuronalnode operation, such as the node model, and synaptic update rules and/orother tasks relevant to network operation. The memory block may beconfigured to store, inter alia, neuronal state variables and connectionparameters (e.g., weights, delays, I/O mapping) of connections 1138.

The micro-blocks 1140 may be interconnected with one another usingconnections 1138 and routers 1136. As it is appreciated by those skilledin the arts, the connection layout in FIG. 11B is exemplary, and manyother connection implementations (e.g., one to all, all to all, and/orother maps) are compatible with the disclosure.

The neuromorphic apparatus 1130 may be configured to receive input(e.g., visual input) via the interface 1142. In one or moreimplementations, applicable for example to interfacing with computerizedspiking retina, or image array, the apparatus 1130 may provide feedbackinformation via the interface 1142 to facilitate encoding of the inputsignal.

The neuromorphic apparatus 1130 may be configured to provide output viathe interface 1144. Examples of such output may include one or more ofan indication of recognized object or a feature, a motor command (e.g.,to zoom/pan the image array), and/or other outputs.

The apparatus 1130, in one or more implementations, may interface toexternal fast response memory (e.g., RAM) via high bandwidth memoryinterface 1148, thereby enabling storage of intermediate networkoperational parameters. Examples of intermediate network operationalparameters may include one or more of spike timing, neuron state, and/orother parameters. The apparatus 1130 may interface to external memoryvia lower bandwidth memory interface 1146 to facilitate one or more ofprogram loading, operational mode changes, retargeting, and/or otheroperations. Network node and connection information for a current taskmay be saved for future use and flushed. Previously stored networkconfiguration may be loaded in place of the network node and connectioninformation for the current task, as described for example in co-pendingand co-owned U.S. patent application Ser. No. 13/487,576 entitled“DYNAMICALLY RECONFIGURABLE STOCHASTIC LEARNING APPARATUS AND METHODS”,filed Jun. 4, 2012, incorporated herein by reference in its entirety.External memory may include one or more of a Flash drive, a magneticdrive, and/or other external memory.

FIG. 11C illustrates one or more implementations of shared busneuromorphic computerized system 1145 comprising micro-blocks 1140,described with respect to FIG. 11B, supra. The system 1145 of FIG. 11Cmay utilize shared bus 1147, 1149 to interconnect micro-blocks 1140 withone another.

FIG. 11D illustrates one implementation of cell-based neuromorphiccomputerized system architecture configured to optical flow encodingmechanism in a spiking network is described in detail. The neuromorphicsystem 1150 may comprise a hierarchy of processing blocks (cellsblocks). In some implementations, the lowest level L1 cell 1152 of theapparatus 1150 may comprise logic and memory blocks. The lowest level L1cell 1152 of the apparatus 1150 may be configured similar to the microblock 1140 of the apparatus shown in FIG. 11B. A number of cell blocksmay be arranged in a cluster and may communicate with one another vialocal interconnects 1162, 1164. Individual clusters may form higherlevel cell, e.g., cell L2, denoted as 1154 in FIG. 11d . Similarly,several L2 clusters may communicate with one another via a second levelinterconnect 1166 and form a super-cluster L3, denoted as 1156 in FIG.11D. The super-clusters 1154 may communicate via a third levelinterconnect 1168 and may form a next level cluster. It will beappreciated by those skilled in the arts that the hierarchical structureof the apparatus 1150, comprising four cells-per-level, is merely oneexemplary implementation, and other implementations may comprise more orfewer cells per level, and/or fewer or more levels.

Different cell levels (e.g., L1, L2, L3) of the apparatus 1150 may beconfigured to perform functionality various levels of complexity. Insome implementations, individual L1 cells may process in paralleldifferent portions of the visual input (e.g., encode individual pixelblocks, and/or encode motion signal), with the L2, L3cells performingprogressively higher level functionality (e.g., object detection).Individual ones of L2, L3, cells may perform different aspects ofoperating a robot with one or more L2/L3 cells processing visual datafrom a camera, and other L2/L3 cells operating motor control block forimplementing lens motion what tracking an object or performing lensstabilization functions.

The neuromorphic apparatus 1150 may receive input (e.g., visual input)via the interface 1160. In one or more implementations, applicable forexample to interfacing with computerized spiking retina, or image array,the apparatus 1150 may provide feedback information via the interface1160 to facilitate encoding of the input signal.

The neuromorphic apparatus 1150 may provide output via the interface1170. The output may include one or more of an indication of recognizedobject or a feature, a motor command, a command to zoom/pan the imagearray, and/or other outputs. In some implementations, the apparatus 1150may perform all of the I/O functionality using single I/O block (notshown).

The apparatus 1150, in one or more implementations, may interface toexternal fast response memory (e.g., RAM) via a high bandwidth memoryinterface (not shown), thereby enabling storage of intermediate networkoperational parameters (e.g., spike timing, neuron state, and/or otherparameters). In one or more implementations, the apparatus 1150 mayinterface to external memory via a lower bandwidth memory interface (notshown) to facilitate program loading, operational mode changes,retargeting, and/or other operations. Network node and connectioninformation for a current task may be saved for future use and flushed.Previously stored network configuration may be loaded in place of thenetwork node and connection information for the current task, asdescribed for example in co-pending and co-owned U.S. patent applicationSer. No. 13/487,576, entitled “DYNAMICALLY RECONFIGURABLE STOCHASTICLEARNING APPARATUS AND METHODS”, incorporated, supra.

In one or more implementations, one or more portions of the apparatus1150 may be configured to operate one or more learning rules, asdescribed for example in owned U.S. patent application Ser. No.13/487,576 entitled “DYNAMICALLY RECONFIGURABLE STOCHASTIC LEARNINGAPPARATUS AND METHODS”, filed Jun. 4, 2012, incorporated herein byreference in its entirety. In one such implementation, one block (e.g.,the L3 block 1156) may be used to process input received via theinterface 1160 and to provide a reinforcement signal to another block(e.g., the L2 block 1156) via interval interconnects 1166, 1168.

In one or more implementations, networks of the apparatus 1130, 1145,1150 may be implemented using Elementary Network Description (END)language, described for example in U.S. patent application Ser. No.13/239,123, entitled “ELEMENTARY NETWORK DESCRIPTION FOR NEUROMORPHICSYSTEMS”, filed Sep. 21, 2011, and/or High Level NeuromorphicDescription (HLND) framework, described for example in U.S. patentapplication Ser. No. 13/385,938, entitled “TAG-BASED APPARATUS ANDMETHODS FOR NEURAL NETWORKS”, filed Mar. 15, 2012, each of the foregoingincorporated, supra. In one or more implementations, the HLND frameworkmay be augmented to handle event based update methodology described, forexample U.S. patent application Ser. No. 13/588,774, entitled “APPARATUSAND METHODS FOR IMPLEMENTING EVENT-BASED UPDATES IN SPIKING NEURONNETWORK”, filed Aug. 17, 2012, the foregoing being incorporated hereinby reference in its entirety. In some implementations, the networks maybe updated using an efficient network update methodology, described, forexample, U.S. patent application Ser. No. 13/239,259, entitled“APPARATUS AND METHOD FOR PARTIAL EVALUATION OF SYNAPTIC UPDATES BASEDON SYSTEM EVENTS”, filed Sep. 21, 2011 and/or U.S. patent applicationSer. No. 13/385,938, entitled “APPARATUS AND METHODS FOR EFFICIENTUPDATES SPIKING NEURON NETWORKS”, filed Jul. 27, 2012, each of theforegoing being incorporated herein by reference in its entirety.

In some implementations, the HLND framework may be utilized to definenetwork, unit type and location, and/or synaptic connectivity. HLND tagsand/or coordinate parameters may be utilized in order to, for example,define an area of the localized inhibition of the disclosure describedabove

In some implementations, the END may be used to describe and/or simulatelarge-scale neuronal model using software and/or hardware engines. TheEND allows optimal architecture realizations comprising ahigh-performance parallel processing of spiking networks withspike-timing dependent plasticity. Neuronal network configured inaccordance with the END may comprise units and doublets, the doubletsbeing connected to a pair of units.

Adaptive predictor and control methodology described herein mayadvantageously enable training of robotic controllers. Previouslylearned actions (primitives) may be reused in subsequent actions thatmay comprise the same and/or similar control operations. A hierarchy ofcontrol actions (primitives) may be developed so as to enable a singlehigher-level action indication (by an operator) to invoke execution two(or more) lower level by the predictor actions without necessitatinggeneration of the explicit control instructions by the operator. By wayof an illustration, a task of teaching a robot to reach for an objectmay be partitioned into two or more (simpler) sub-tasks: e.g., approachtarget and/or avoid obstacles. In turn, individual tasks approach targetand/or avoid obstacles may be partitioned into a sequence of robotmovements (e.g., turn left/right, go forward/backwards). One or morepredictors of the robot controller may be trained to perform lowerlevel. Another predictor may be trained to associate a n actionindicator (e.g., approach) with one or more movement tasks. A hierarchyof action primitives may enable an operator to operate the robot toperform composite tasks based on previously learned sub-tasks.

When teaching the controller a new task (behavior of serving a glass ofwater), using the previously learned behaviors and/or primitives(reaching, grasping an object, etc.) may be utilized therebyaccelerating learning compared to methods of the prior art.

One or more predictors may be configured to learn to execute learnedtasks may be When teaching the controller a new task (behavior ofserving a glass of water), using the previously learned behaviors and/orprimitives (reaching, grasping an object, etc.) may be utilized therebyaccelerating learning compared to methods of the prior art.

The learning process of the adaptive predictor may comprise supervisedlearning process, operated in accordance with a teaching input from asupervisor agent. Supervised learning may utilize fewer memory and/orcomputational resources (due to, e.g., a smaller exploration statespace). The computational efficiency may be leveraged to implement morecomplex controller (for given hardware resources) and/or to reducehardware complexity (for a given controller task load).

In one or more obstacle avoidance applications, an adaptive predictorapparatus may be configured to learn to anticipate the obstacles,allowing for faster and smoother anticipatory avoidance behavior.

In one or more object recognition applications, an adaptive predictorapparatus may speed-up and/or improve reliability of object detection inthe presence of noisy and/or otherwise poor sensory information(“pattern completion”.)

Adaptive prediction methodology may provide a means for evaluatingdiscrepancy between the predicted state and the actual state (configuredbased on, e.g., input from the environment) thereby allowing the controlsystem to be sensitive to novel or unexpected stimuli within the robotenvironment.

In some implementations, such discrepancy evaluation may be utilized fornovelty detection. By monitoring the discrepancy, one or more behaviorsthat result in unpredicted, and/or novel results may be identified.Learning of these behaviors may be repeat until these behaviors arelearned (become predictable). In some implementations, the behaviorpredictability may be determined based one the discrepancy being below agiven threshold.

In one or more implementations, training methodology described hereinmay be applied to robots learning their own kinematics and/or dynamics(e.g., by the robot learning how to move its platform). Adaptivecontroller of the robot may be configured to monitor the discrepancy andonce one or more movements in a given region of the working space arelearned, the controller may attempt to learn other movements. In someimplementations, the controller may be configured to learn consequencesrobot actions on the world: e.g. the robot pushes an object and thecontroller learns to predict the consequences (e.g., if the push tooweak nothing may happen (due to friction); if the push is stronger, theobject may start moving with an acceleration being a function of thepush force)

In some sensory-driven implementations, the controller may be configuredto learn associations between observed two or more sensory inputs.

In one or more safety applications, the controller may be configured toobserve action of other robots that may result in states that may bedeemed dangerous (e.g., result in the robot being toppled over) and/orsafe. Such approaches may be utilized in robots learning to move theirbody and/or learning to move or manipulate other objects.

It will be recognized that while certain aspects of the disclosure aredescribed in terms of a specific sequence of steps of a method, thesedescriptions are only illustrative of the broader methods of theinvention, and may be modified as required by the particularapplication. Certain steps may be rendered unnecessary or optional undercertain circumstances. Additionally, certain steps or functionality maybe added to the disclosed implementations, or the order of performanceof two or more steps permuted. All such variations are considered to beencompassed within the disclosure disclosed and claimed herein.

While the above detailed description has shown, described, and pointedout novel features of the disclosure as applied to variousimplementations, it will be understood that various omissions,substitutions, and changes in the form and details of the device orprocess illustrated may be made by those skilled in the art withoutdeparting from the disclosure. The foregoing description is of the bestmode presently contemplated of carrying out the invention. Thisdescription is in no way meant to be limiting, but rather should betaken as illustrative of the general principles of the invention. Thescope of the disclosure should be determined with reference to theclaims.

What is claimed:
 1. A robotic system for maneuvering a robot along apath to avoid objects along the path, comprising: a memory havingcomputer readable instructions stored thereon; and at least oneprocessing device configurable to execute the computer readableinstructions to, associate, during a learning phase, movement of therobot towards a target location based on an association developedbetween an action tag and a sensory context, the action tagcorresponding to an input signal received by the robot, the sensorycontext corresponding to data collected by at least one sensor on therobot, and generate, during an operation phase, an output signalconfigured to cause the robot to perform a desired action in absence ofa contemporaneous input signal, the operation phase being different fromthe learning phase, the output signal being a combination of first andsecond signals each including a number of channels, the output signalincluding a number of channels fewer than the number of channels in eachof the first and second signals.
 2. The robotic system of claim 1,wherein the sensory context corresponds to location of the objects withrespect to the robot.
 3. The robotic system of claim 1, wherein theaction tag corresponds to either a left turn or a light turn proximateto the object.
 4. The robotic system of claim 1, wherein the firstsignal includes channels of N-dimension, the first signal corresponds toinformation stored in a look-up table.
 5. The robotic system of claim 4,wherein the second signal includes channels of M-dimension differentfrom channels of N-dimension.
 6. The robotic system of claim 1, whereinthe at least one processing device configurable to execute the computerreadable instructions to, distinguish higher-order control actions fromlower-order control actions based on the sensory context and associationdeveloped, the higher-order control actions include at least one ofapproach, fetch, or avoid an object detected along the path.
 7. A methodfor maneuvering a robot along a path to avoid objects along the path,comprising: associating, during a learning phase; movement of the robottowards a target location based on an association developed between anaction tag and a sensory context, the action tag corresponding to aninput signal received by the robot, the sensory context corresponding todata collected by at least one sensor on the robot; and generating,during an operation phase, an output signal configured to cause therobot to perform a desired action in absence of a contemporaneous inputsignal, the operation phase being different from the learning phase, theoutput signal being a combination of first and second signals eachincluding a number of channels, the output signal including a number ofchannels fewer than the number of channels in each of the first andsecond signals.
 8. A non-transitory computer readable medium comprisingcomputer readable instructions stored thereon, that when executed by atleast one processing device configured the at least one processingdevice to, associate, during a learning phase, movement of the robottowards a target location based on an association developed between anaction tag and a sensory context, the action tag corresponding to aninput signal received by the robot, the sensory context corresponding todata collected by at least one sensor on the robot; and generate, duringan operation phase, an output signal configured to cause the robot toperform a desired action in absence of a contemporaneous input signal,the operation phase being different from the learning phase, the outputsignal being a combination of first and second signals each including anumber of channels, the output signal including a number of channelsfewer than the number of channels in each of the first and secondsignals.
 9. The method of claim 7, wherein the sensory contextcorresponds to location of the objects with respect to the robot. 10.The method of claim 7, wherein the action tag corresponds to either aleft turn or a right turn proximate to the object.
 11. The method ofclaim 7, wherein the first signal includes channels of N-dimension, thefirst signal corresponds to information stored in a look-up table. 12.The method of claim 11, wherein the second signal includes channels ofM-dimension different from channels of N-dimension.
 13. The method ofclaim 7, further comprising distinguishing higher-order control actionsfrom lower-order control actions based on the sensory context andassociation developed, wherein the higher-order control actions includeat least one of approach, fetch, or avoid an object detected along thepath.
 14. The non-transitory computer readable medium of claim 8,wherein the sensor context corresponds to location of the objects withrespect to the robot.
 15. The non-transitory computer readable medium ofclaim 8, wherein the action tag corresponds to either a left turn or aright turn proximate to the object.
 16. The non-transitory computerreadable medium of claim 8, wherein the first signal includes channelsof N-dimension, the first signal corresponds to information stored in alook-up table.
 17. The non-transitory computer readable medium of claim8, wherein the second signal includes channels of M-dimension differentfrom channels of N-dimension.
 18. The non-transitory computer readablemedium of claim 8, wherein the at least one processing device is furtherconfigurable to, distinguishing higher-order control actions fromlower-order control actions based on the sensory context and associationdeveloped, wherein the higher-order control actions include at least oneof approach, fetch, or avoid an object detected along the path.